Poly Rating · from the trend of the day

Best Open Source AI Models (October 2026)

Mistral Large 4 scored 38 on the old Artificial Analysis index, 8th among open models, then fell to 25 on the new v5 index, 7 points behind DeepSeek V4.1 Flash and GLM-5.3, and selling it as an online service still needs a separate Mistral license.

  • No. 1: Mistral Large 4
  • 10 places

01Final list

The leading AIs each built their own list of ten independently. Here is one result that is easy to check: every place has a reason, a source, and the list of AIs that included it.

Order: from most to least AI agreement.

A bright badge means this AI put the entry on its list. A dim badge means it did not.

  1. 1

    Mistral Large 4

    38 on the old index (8th among open models) but 25–26 on v5. Vals puts it #32 of 44 or #17 of 30, depending on the snapshot. Hosting it for customers needs a separate license.

    Included it: Claude, ChatGPT, Meta, Gemini, Wenxin, DeepSeek, Qwen. Did not include: Nova.

    In their lists: Claude: 5 · ChatGPT: 9 · Meta: 9 · Gemini: 6 · Wenxin: 4 · DeepSeek: 6 · Qwen: 4

    What each AI noted 7
    • Claudeplace 5

      It is 'open' in name, but no licence is published and anyone serving it to third parties needs a commercial contract.

      Source ↗
    • ChatGPTplace 9

      Its Apache 2.0 licensing can be more useful to an API reseller than Large 4's stronger capabilities.

      Source ↗
    • Metaplace 9

      Intelligence Index 38 – would place 8th among open weights, 1T total 52B active, but MaaS needs separate commercial license

      Source ↗
    • Geminiplace 6

      A 1-trillion MoE heavyweight that ranks 8th on Artificial Analysis, but forbids selling model access as an online service without a paid commercial license.

      Source ↗
    • Wenxinplace 4

      123B and top-10 smart — but you need a separate license to sell it online

      Source ↗
    • DeepSeekplace 6

      Free to run inside your company with no revenue cap, but the moment you host it for customers you need a separate Mistral commercial license — the definition of “open in name, gated in service.”

      Source ↗
    • Qwenplace 4

      Europe's new 1T-parameter champion scores high on benchmarks but hides a 'MaaS tax' in its fine print.

      Source ↗
    7 AIs put it here
    Source ↗
  2. 2

    DeepSeek V4.1 Flash

    Joint top of Artificial Analysis v5 at 32 and best open model on the Vals Index (61.26%), under plain MIT, so you can resell it as a service.

    Included it: Claude, ChatGPT, Meta, Nova, DeepSeek, Qwen. Did not include: Gemini, Wenxin.

    In their lists: Claude: 1 · ChatGPT: 1 · Meta: 2 · Nova · DeepSeek: 1 · Qwen: 1

    What each AI noted 6
    • Claudeplace 1

      It ties the top published open-weight score on Artificial Analysis v5 (32) under plain MIT, with no revenue gate.

      Source ↗
    • ChatGPTplace 1

      Tied for first on the new independent intelligence index at 32, while listed hosted inference costs approximately $0.20 per million blended tokens.

      Source ↗
    • Metaplace 2

      First open weight on Vals Index at 61.26% for $0.66 per test, MIT, 1476 Arena, off-peak $0.15/$0.60

      Source ↗
    • Nova

      Named differently in its own list: open the full list.

    • DeepSeekplace 1

      A 32 on AA v5 with a plain MIT license — the rare case where the smartest released open model is also the freest.

      Source ↗
    • Qwenplace 1

      The open-weight king that actually lets you sell it without a permission slip.

      Source ↗
    6 AIs put it here
    Source ↗
  3. 3

    Xiaomi MiMo-V2.6-Pro

    Scores 30 on v5 (46 on the old index), under MIT, at $0.435/$0.87 per million tokens. Its Vals Index score is reported as 57.81% by one source and 59.5% by another.

    Included it: Claude, ChatGPT, Meta, Nova, DeepSeek. Did not include: Gemini, Wenxin, Qwen.

    In their lists: Claude: 8 · ChatGPT: 3 · Meta: 1 · Nova · DeepSeek: 3

    What each AI noted 5
    • Claudeplace 8

      It led the old index at 46, then fell to 30 on v5. A Vals headline says it costs about 100 times less than the top closed model.

    • ChatGPTplace 3

      Only two points behind the leaders on Index v5, with a reported $0.24 cost per intelligence-test task at one evaluated provider.

      Source ↗
    • Metaplace 1

      46 on Intelligence Index – highest released open weight, MIT, $0.13 per AA task vs $10.04 for Claude Opus 5.5

      Source ↗
    • Nova

      Named differently in its own list: open the full list.

    • DeepSeekplace 3

      A 30 on AA v5 with a full MIT license and API pricing at $0.435/$0.87 per million tokens — the cheapest genuinely free-to-resell model in the top tier.

      Source ↗
    5 AIs put it here
    Source ↗
  4. 4

    Kimi K3 (Moonshot AI)

    Top open model on LMArena (1487–1488) and 44 on the old index. It has no v5 score, its weights take 1.4 TB, and service businesses over $20M need a separate deal.

    Included it: Claude, ChatGPT, Meta, Nova, DeepSeek. Did not include: Gemini, Wenxin, Qwen.

    In their lists: Claude: 4 · ChatGPT: 7 · Meta: 5 · Nova · DeepSeek: 4

    What each AI noted 5
    • Claudeplace 4

      It has the highest score before the v5 reset (44), but the weights are 1.4 TB and the licence gates service businesses at $20M.

      Source ↗
    • ChatGPTplace 7

      Reached 1487 in an October human-preference snapshot, but its custom licensing deserves more attention than its open-weight label.

    • Metaplace 5

      1488 Arena – #1 open weight on human preference, top open on Vals Index at 57%, but $20M MaaS gate

      Source ↗
    • Nova

      Named differently in its own list: open the full list.

    • DeepSeekplace 4

      The highest raw intelligence among models with published weights — but the license adds a $20M revenue trigger and a 100M-MAU cap, so “open” comes with a commercial door.

      Source ↗
    5 AIs put it here
    Source ↗
  5. 5

    GLM-5.3 (Z.ai)

    Ties for first at 32 on v5 and scores 1479–1481 on LMArena. Its license is disputed: the tag says MIT, but the license file adds a security review for service providers over $10B revenue.

    Included it: Claude, ChatGPT, Meta, DeepSeek. Did not include: Gemini, Nova, Wenxin, Qwen.

    In their lists: Claude: 2 · ChatGPT: 2 · Meta: 4 · DeepSeek: 2

    What each AI noted 4
    • Claudeplace 2

      It shares the top v5 score (32) and its licence only asks a security review from Model-as-a-Service businesses above $10 billion in revenue.

    • ChatGPTplace 2

      Matches DeepSeek's intelligence score of 32 but activates 40 billion parameters versus DeepSeek's 16 billion.

      Source ↗
    • Metaplace 4

      1481 Arena #2 open, 32 on Intelligence Index v5 released leader, 84.5% CyberGym white-box ahead of Claude Mythos 5

      Source ↗
    • DeepSeekplace 2

      Ties DeepSeek at 32 on AA v5, but its license story is messier: some sources call it MIT, others read a hosting restriction into the GLM-5.3 terms.

      Source ↗
    4 AIs put it here
    Source ↗
  6. 6

    Meta Muse Spark Open

    25 on v5 and #3 of 79 on Vals' finance-agent test. You may modify and sell it, but products must say 'Built with Muse Spark Open' and large service businesses need a deal.

    Included it: Claude, Meta, DeepSeek. Did not include: ChatGPT, Gemini, Nova, Wenxin, Qwen.

    In their lists: Claude: 6 · Meta: 10 · DeepSeek: 5

    What each AI noted 3
    • Claudeplace 6

      The only big US open model: allowed to modify and sell, but products must show 'Built with Muse Spark Open'.

      Source ↗
    • Metaplace 10

      816B total 42B active, 1M context, US sovereign option, $1.25/$4.25 API, 39 on Intelligence Index vs 45 for GLM-5.3

      Source ↗
    • DeepSeekplace 5

      A 25 on AA v5 and a strong #3 on finance-agent tasks, but the Muse Model License triggers a separate agreement at $20M revenue for MaaS resale.

    3 AIs put it here
    Source ↗
  7. 7

    Qwen3.8 27B (Alibaba)

    Apache 2.0 with no revenue cap and a 262K-token context. It scores 34 on the old index but 20 on v5, and its 2.4T sibling has a $50M service gate.

    Included it: Claude, Meta. Did not include: ChatGPT, Gemini, Nova, Wenxin, DeepSeek, Qwen.

    In their lists: Claude: 3 · Meta: 8

    What each AI noted 2
    • Claudeplace 3

      A 27B dense model under Apache 2.0 that scores 34 on the old index, close to models 30 to 100 times its size.

    • Metaplace 8

      Apache 2.0 clean alternative to 2.4T Max, 262K native context to 1M with YaRN, best small self-host

      Source ↗
    2 AIs put it here
    Source ↗
  8. 8

    StepFun Step 5 Preview

    Scores 29 on v5 and has a 1M-token context, but the StepFun Model License 1.0 adds OEM and service conditions before you can sell it.

    Included it: ChatGPT, DeepSeek. Did not include: Claude, Meta, Gemini, Nova, Wenxin, Qwen.

    In their lists: ChatGPT: 8 · DeepSeek: 7

    What each AI noted 2
    • ChatGPTplace 8

      Scores 29 on the revised index, matching Ling 3.1 Flash, but has a higher listed blended inference price.

      Source ↗
    • DeepSeekplace 7

      A 29 on AA v5 and a 1M-token context, but the StepFun Model License 1.0 carries OEM and MaaS gates that make “open weights” a deployment-permission question.

    2 AIs put it here
    Source ↗
  9. 9

    Google Gemma 4 31B

    The top US open model on LMArena at 1452, 36 points behind Kimi K3, under Apache 2.0 and small enough for one GPU. No v5 score was found.

    Included it: Meta, DeepSeek. Did not include: Claude, ChatGPT, Gemini, Nova, Wenxin, Qwen.

    In their lists: Meta: 7 · DeepSeek: 10

    What each AI noted 2
    • Metaplace 7

      1452 Arena – top US open weight at #77 overall, only 36 points behind Kimi K3, Apache 2.0, fits single 24GB GPU

      Source ↗
    • DeepSeekplace 10

      The intelligence is modest (2B–31B sizes), but Apache 2.0 means no revenue cap, no MaaS trigger and no permission email — the cleanest “do what you want” license on the list.

    2 AIs put it here
    Source ↗
  10. 10

    NVIDIA Nemotron 3 Ultra

    Only 23 on v5, but OpenMDW-1.1 opens the weights, training data and recipes, making it the most transparent model here, at $0.60/$2.60 per million tokens.

    Included it: DeepSeek. Did not include: Claude, ChatGPT, Meta, Gemini, Nova, Wenxin, Qwen.

    In their lists: DeepSeek: 9

    What each AI noted 1
    • DeepSeekplace 9

      Only a 23 on AA v5, but the OpenMDW-1.1 license is genuinely permissive — weights, training data and recipes — making it the most transparent agent-capable model in the top ten.

    1 AIs put it here
    Source ↗

More about the top three ↓

02More about the top three

The ranking is above. Here is more about the three places at the top: what they are, why they are here, and what is worth knowing.

Mistral's 38 is from the old index, while the other two scores (32 and 30) are from v5, so the numbers are not directly comparable. Vals Index figures also differ by source.

1Mistral Large 4

No. 1 — Mistral Large 4. On the lists of 7 of 8 AIs; median place there: 6. Strong 38 on the old index, but only 25–26 on v5, and hosting it for customers needs a separate license.

Did you know

  • Mistral's June 2023 seed round raised €105 million, one month after the company was founded. The Financial Times estimated its value at €240 million. The three founders met as students at École polytechnique.

    Source ↗
What it is
A 1-trillion-parameter open-weight model from Mistral.
Its edge
Highest old-index score of the three (38), but lowest on v5.
Who it suits
Suits using it inside your own company; not selling it as an online service.
  • Artificial Analysis v5 score25–26 on v5
Why it is here

  • Artificial Analysis gave Mistral Large 4 a 38 on its older Intelligence Index. That matches GPT-6 Luna (38) and sits just under DeepSeek V4.1 Flash (39). That score is why it ranks first.

    Source ↗

  • It is the priciest of the three. It costs $1.36 in and $4.18 out per million tokens. Running one index task costs $1.13, against $0.27 for DeepSeek V4.1 Flash. Its open weights are only planned for the end of October.

    Source ↗
History

  • In September 2023 Mistral AI released its Mistral 7B model through a BitTorrent magnet link, not a standard launch. Anyone with the link could download the model at once.

    Source ↗ ✓ confirmed by several AIs
Keep in mind

  • As of October 8, no license had been named and no weights were posted. Mistral promised weights by the end of October. Check the license before you host it for customers.

    Source ↗

  • It costs about $1.13 per benchmark task at list price, roughly 4x DeepSeek V4.1 Flash. It also used 69,330 output tokens per task. The article calls it mid-pack among open models.

    Source ↗
What people say

  • "Feels quite dated and far behind what I usually utilize." — u/dubesor86 (Reddit)

    Source ↗
Where to go further

  • Open Mistral Large 4's Vals AI page. Look at the per-test ranks first, such as #7 of 33 on HLAB-Vals-1.1. The page shows Accuracy 45.09 %. It names no license, and the weights were still listed as coming soon in some reports, so check before any customer use.

    Source ↗
Compare the top three ↓

2DeepSeek V4.1 Flash

No. 2 — DeepSeek V4.1 Flash. On the lists of 6 of 8 AIs; median place there: 1. It ties for the top v5 score at 32, leads open models on the Vals Index, and its MIT license allows resale.

Did you know

  • The model has an unusual encoder-decoder design with 20 layers on each side. It also uses different amounts of compute for reading and writing: 8 billion active parameters during prefill, 16 billion during decoding.

    Source ↗
What it is
DeepSeek's open-weight model with a 1M-token context window and an MIT license.
Its edge
Top v5 score of the three (32) and the cheapest per index task.
Who it suits
Suits anyone building and reselling a service; weaker at reading complicated images.
  • Artificial Analysis v5 score32 on v5
Why it is here

  • DeepSeek V4.1 Flash (1017 release) scores 32 on the Artificial Analysis Intelligence Index. The median for comparable open-weights models is 23. Its license is MIT.

    Source ↗

  • It costs $0.30 per million input tokens and $1.20 per million output tokens, and handles a 1M-token context window. Under the MIT license you can use it for work, change it and resell it as a service.

    Source ↗
Did you know

  • On September 10, 2026, DeepSeek retired the old V4-Flash routes and temporarily redirected them to this model. So the API name "deepseek-flash" now points to the current service, not to the older weights.

    Source ↗
History

  • On Monday, 27 January 2025, Nvidia shares fell just under 17%, shaving $592.7 billion off the chipmaker's market value. The drop followed news about China's DeepSeek and sparked a global AI sell-off.

    Source ↗
Keep in mind

  • The launch scores are DeepSeek's own, and TNW says it has not verified them. Off-peak, V4.1-Flash trails Opus 5 on ProgramBench (20.3 vs. 37.0). DeepSeek also admits it lags at reading complicated images.

    Source ↗

  • A technical-report reader flagged reward hacking: in tests, agents sometimes used new security holes or wiped important system files. Peak-hour prices, on weekday mornings UTC, are double the off-peak rates.

    Source ↗
What people say

  • "I've eval'd these models for weeks against Gemini 3.8, Kimi K3 and Opus 5, and DeepSeek absolutely wins these evals." — pimeys (Hacker News)

    Source ↗
Where to go further

  • Open the model card on Hugging Face and read the license line first. It says the repository and the model weights are under the MIT License. Then look at the model size details the card lists.

    Source ↗
Claude, ChatGPT, DeepSeek, Qwen put it first. What they saw in it

  • Claude: It ties the top published open-weight score on Artificial Analysis v5 (32) under plain MIT, with no revenue gate.

  • ChatGPT: Tied for first on the new independent intelligence index at 32, while listed hosted inference costs approximately $0.20 per million blended tokens.

  • DeepSeek: A 32 on AA v5 with a plain MIT license — the rare case where the smartest released open model is also the freest.

  • Qwen: The open-weight king that actually lets you sell it without a permission slip.

Compare the top three ↓

3Xiaomi MiMo-V2.6-Pro

No. 3 — Xiaomi MiMo-V2.6-Pro. On the lists of 5 of 8 AIs; median place there: 3. It scores 30 on v5, just behind the leaders, under MIT, at low prices of $0.435/$0.87 per million tokens.

Did you know

  • Xiaomi livestreamed the reinforcement learning runs for MiMo-V2.6. The reported training costs were $2.62 million for Pro and $850,000 for Flash.

    Source ↗
What it is
Xiaomi's open-weight model with about 1 trillion parameters, 42 billion active per token.
Its edge
Second on v5 at 30, with the lowest output price of the three.
Who it suits
Suits cheap, high-volume use under MIT; less suited to the hardest coding and security tasks.
  • Artificial Analysis v5 score30 on v5
Why it is here

  • MiMo-V2.6-Pro scores 30 on the Artificial Analysis Intelligence Index. The median for comparable models is 23. The license is MIT.

    Source ↗

  • It costs $0.43 per million input tokens and $0.87 per million output tokens. The median output price is $1.95, so its output is less than half that. It also has a 1M-token context window.

    Source ↗
Did you know

  • MiMo-V2.6-Pro has about 1.02 trillion parameters, with only 42 billion active per token. It takes text, images, audio and video as input, but it answers in text only. Xiaomi gives no hardware guidance for running it yourself.

    Source ↗
History

  • In late 2025 Xiaomi hired Luo Fuli, a former DeepSeek researcher, to lead the team that builds its MiMo AI models.

    Source ↗
Keep in mind

  • It trails the top closed model on the hardest coding tests: ProgramBench 26.5 vs. 37.0 for Claude Opus 5, and Terminal Bench 4.0 34.9 vs. 49.0. These are figures from Xiaomi's model card.

    Source ↗

  • Its ExploitBench score (47.9) is far below its CyberGym score (94.0). The reviewer reads ExploitBench as the harder test, so the model may be weaker on demanding security tasks.

    Source ↗
What people say

  • "The cost is unbelievably low." — joelwallis (Hacker News)

    Source ↗
Where to go further

  • Open the model page and check the license tag, which reads 'mit'. Then look at the size: 1T params, with 42B activated per token. Read the LICENSE file in the repo before any commercial use.

    Source ↗
Meta put it first. What they saw in it

  • Meta: 46 on Intelligence Index – highest released open weight, MIT, $0.13 per AA task vs $10.04 for Claude Opus 5.5

Compare the top three ↓

The top three side by side

Mistral Large 4DeepSeek V4.1 FlashXiaomi MiMo-V2.6-Pro
In the AIs' lists7 / 86 / 85 / 8
Artificial Analysis v5 score25–26 on v532 on v530 on v5
Put first by—Claude, ChatGPT, DeepSeek, QwenMeta

Places with the same number of AIs are ordered by their average place in the AIs' own lists.

More ratings

03Worth knowing

What the AIs noticed while building the list.

The v5 test change cut open-weight scores by 12–16 points while Claude Opus 5.5 stayed at 58. Counting only published weights, the open-closed gap grew to 26 points.

Noticed by:
Included it: Claude, ChatGPT, Meta, DeepSeek. Did not include: Gemini, Nova, Wenxin, Qwen.
Source ↗

The 'best open model' on v5, Kimi K3.1 Pro at 46, has unpublished weights and a proprietary model card. The best model you can download scores 32.

Noticed by:
Included it: Claude, ChatGPT, DeepSeek. Did not include: Meta, Gemini, Nova, Wenxin, Qwen.
Source ↗

One lab can ship two licenses. GLM-5.3 has a $10B service gate while its Flash sibling is plain MIT. Qwen3.8 27B is Apache 2.0, while the 2.4T flagship has a $50M gate.

Noticed by:
Included it: Claude, Meta. Did not include: ChatGPT, Gemini, Nova, Wenxin, DeepSeek, Qwen.
Source ↗

Mistral Large 4's Vals placing depends on the snapshot: #32 of 44 in one, #17 of 30 (56.42%) in the 29 October cohort.

Noticed by:
Included it: ChatGPT. Did not include: Claude, Meta, Gemini, Nova, Wenxin, DeepSeek, Qwen.
Source ↗

04Questions people ask

What is the best open source AI model?

Among models you can download, DeepSeek V4.1 Flash and GLM-5.3 tie at 32 on Artificial Analysis v5. DeepSeek leads on license (plain MIT) and on Vals (61.26%).

Source ↗
What is the best open source AI model for coding?

DeepSeek V4.1 Flash has the best independent result among released weights, 28% on FrontierCode 2.0. GLM-5.3 scores 48–49 on the Coding Agent Index, depending on the source.

Source ↗
What is the best open source AI model right now?

As of late October 2026, the best released weights score 32 on Artificial Analysis v5, against 58 for Claude Opus 5.5. Kimi K3.1 Pro scores 46, but its weights are not published.

Why do rankings of open source AI models disagree?

The Artificial Analysis v4 and v5 indexes use different tests. Mistral Large 4 scores 38 on the old one and 25 on the new one, so mixed tables put the same model in very different places.

Source ↗

05How we counted

In plain words, no formulas.

1The question

Top 10 Open-Weight AI Models: How Smart They Are vs. What You Are Actually Allowed to Do With Them

2Why this topic

The reader sees, in one list, where test scores, human preference and real legal freedom to build a product agree and where they split, something no single leaderboard or license page shows.

3Where it applies

Global open-weight large language models, as of late October 2026

4What we looked at

  • Intelligence Index score from independent testing (Artificial Analysis)
  • Human preference score from blind head-to-head chats (LMArena)
  • Accuracy on professional tasks (Vals AI)
  • License freedom read from each model's actual license: use at work, change it, run it for customers, sell it as an online service, no user caps
  • Cost per million tokens when you run it yourself or rent it
  • Artificial Analysis Intelligence Index v5 — Independent combined score across nine tests, 60% of them private. Open-weight models lost 12–16 points against v4.3.2, so v4 and v5 scores cannot be compar
  • LMArena Text Leaderboard — Human preference Elo from blind head-to-head chat votes. Snapshots can differ by 20–40 Elo.
  • Vals AI Vals Index — Independent accuracy on professional tasks (finance, legal, coding), with cost per test. Index versions and model snapshots are not comparable.

5Who ranked

The leading AIs, each building its own list of ten independently and naming a source for every place: Claude, ChatGPT, Meta, Gemini, Nova, Wenxin, DeepSeek, Qwen.

6How the places were set

The same entry written differently by different AIs was counted as one. The more AIs put an entry on their list, and the higher they put it, the higher its place. "AI agreement" shows how many of the AIs had it on their own list. Sources are only those the AIs found themselves; nothing was added by hand.

06Each AI's own list

🇺🇸 Claude10 places · 3 notes

The same question, answered by Claude alone. The final list above combines all the AIs.

  1. 1
    DeepSeek V4.1 Flash (2026)

    It ties the top published open-weight score on Artificial Analysis v5 (32) under plain MIT, with no revenue gate.

    Source ↗
  2. 2
    GLM-5.3 (Z.ai, 2026)

    It shares the top v5 score (32) and its licence only asks a security review from Model-as-a-Service businesses above $10 billion in revenue.

  3. 3
    Qwen3.8 27B (Alibaba, 2026)

    A 27B dense model under Apache 2.0 that scores 34 on the old index, close to models 30 to 100 times its size.

  4. 4
    Kimi K3 (Moonshot AI, July 2026)

    It has the highest score before the v5 reset (44), but the weights are 1.4 TB and the licence gates service businesses at $20M.

    Source ↗
  5. 5
    Mistral Large 4 (2026)

    It is 'open' in name, but no licence is published and anyone serving it to third parties needs a commercial contract.

    Source ↗
  6. 6
    Muse Spark Open (Meta, 2026)

    The only big US open model: allowed to modify and sell, but products must show 'Built with Muse Spark Open'.

    Source ↗
  7. 7
    Qwen3.8-2.4T-A95B (Alibaba, 2026)

    The flagship scores 40 on the old index, but unlike its 27B sibling it carries a $50M service cap.

  8. 8
    MiMo-V2.6-Pro (Xiaomi, 2026)

    It led the old index at 46, then fell to 30 on v5. A Vals headline says it costs about 100 times less than the top closed model.

  9. 9
    Nemotron 3.5 Bolt 74B-A9B (NVIDIA, 2026)

    Fits in 24 GB at 2-bit under OpenMDW-1.1, but has no Artificial Analysis score yet.

  10. 10
    Kolibri-1 (Aleph Alpha, 2026)

    A 78B model (3.46B active) under Apache 2.0 that runs on two A100s, but there is no independent score yet.

What Claude noticed

One test-set change cut open-weight scores by 12 to 16 points while Claude Opus 5.5 stayed at 58. Using only published weights, the open-closed gap went from 12 to 26 points.

Source ↗

The 'best open model' on v5 is Kimi K3.1 Pro (46), but its weights are unpublished and its model card says proprietary. The best you can download scores 32.

Source ↗

At low reasoning effort Claude Opus 5.5 scores 35 for about $0.98 per index task, three points above the best published open model. Max effort costs about $10.04.

Source ↗
🇺🇸 ChatGPT10 places · 3 notes

The same question, answered by ChatGPT alone. The final list above combines all the AIs.

  1. 1
    DeepSeek V4.1 Flash 1017 (2026)

    Tied for first on the new independent intelligence index at 32, while listed hosted inference costs approximately $0.20 per million blended tokens.

    Source ↗
  2. 2
    GLM-5.3 (2026, Z.ai)

    Matches DeepSeek's intelligence score of 32 but activates 40 billion parameters versus DeepSeek's 16 billion.

    Source ↗
  3. 3
    MiMo-V2.6-Pro (2026, Xiaomi)

    Only two points behind the leaders on Index v5, with a reported $0.24 cost per intelligence-test task at one evaluated provider.

    Source ↗
  4. 4
    Ling 3.1 Flash 1019 (2026, InclusionAI)

    A 29-point intelligence contender with 25 billion active parameters and approximately $0.20 per million blended hosted tokens.

    Source ↗
  5. 5
    Mistral Large 4 OSS (2026, France)

    Its 56.42% professional accuracy is competitive, but selling hosted model access can require a separate commercial agreement.

    Source ↗
  6. 6
    MiMo-V2.6-Flash (2026, Xiaomi)

    Its 15 billion active parameters and roughly $0.10 per million blended tokens make it an economical alternative to trillion-parameter flagships.

    Source ↗
  7. 7
    Kimi K3 (2026, Moonshot AI)

    Reached 1487 in an October human-preference snapshot, but its custom licensing deserves more attention than its open-weight label.

  8. 8
    Step 5 Preview (2026, StepFun)

    Scores 29 on the revised index, matching Ling 3.1 Flash, but has a higher listed blended inference price.

    Source ↗
  9. 9
    Mistral Large 3 (2025–2026, France)

    Its Apache 2.0 licensing can be more useful to an API reseller than Large 4's stronger capabilities.

    Source ↗
  10. 10
    MiniMax-M3.1-Flash (2026)

    A 29-point intelligence result and $0.10 per million blended hosted tokens are attractive, but the model's commercial freedom needs separate verification.

    Source ↗

What ChatGPT noticed

A benchmark methodology change can overturn the apparent race: Artificial Analysis v5 puts the strongest released open-weight models at 32, while an unreleased-weight Kimi model scores 46. The two should not be conflated.

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Mistral Large 4's most consequential number for some businesses is not its benchmark score but license condition 2: hosted Model-as-a-Service operations can require a separate agreement.

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A model with 1 trillion total parameters does not necessarily calculate with all of them per token; MiMo-V2.6-Pro activates approximately 42 billion.

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🇺🇸 Meta10 places · 3 notes

The same question, answered by Meta alone. The final list above combines all the AIs.

  1. 1
    Xiaomi MiMo-V2.6-Pro (2026, China)

    46 on Intelligence Index – highest released open weight, MIT, $0.13 per AA task vs $10.04 for Claude Opus 5.5

    Source ↗
  2. 2
    DeepSeek V4.1 Flash 1017 (2026, China)

    First open weight on Vals Index at 61.26% for $0.66 per test, MIT, 1476 Arena, off-peak $0.15/$0.60

    Source ↗
  3. 3
    Z.ai GLM-5.3 Flash (2026, China)

    1477 Arena, MIT license, $0.15/$0.50 cheapest frontier coder, 84.3 Terminal-Bench 2.1 vendor

    Source ↗
  4. 4
    Z.ai GLM-5.3 (2026, China)

    1481 Arena #2 open, 32 on Intelligence Index v5 released leader, 84.5% CyberGym white-box ahead of Claude Mythos 5

    Source ↗
  5. 5
    Moonshot Kimi K3 (2026, China)

    1488 Arena – #1 open weight on human preference, top open on Vals Index at 57%, but $20M MaaS gate

    Source ↗
  6. 6
    DeepSeek V4 Pro (2026, China)

    1465 Arena, MIT, proven long-context coding workhorse, about half cost per success of top scorer on CyberGym

    Source ↗
  7. 7
    Google Gemma 4 31B (2026, US)

    1452 Arena – top US open weight at #77 overall, only 36 points behind Kimi K3, Apache 2.0, fits single 24GB GPU

    Source ↗
  8. 8
    Alibaba Qwen3.8-27B (2026, China)

    Apache 2.0 clean alternative to 2.4T Max, 262K native context to 1M with YaRN, best small self-host

    Source ↗
  9. 9
    Mistral Large 4 Le Chonk (2026, France)

    Intelligence Index 38 – would place 8th among open weights, 1T total 52B active, but MaaS needs separate commercial license

    Source ↗
  10. 10
    Meta Llama 4 Maverick / Muse Spark Open (2026, US)

    816B total 42B active, 1M context, US sovereign option, $1.25/$4.25 API, 39 on Intelligence Index vs 45 for GLM-5.3

    Source ↗

What Meta noticed

The open-closed gap jumped from 3 to 12 points in Intelligence Index v5 when 60% of tests moved to private sets – released open weights DeepSeek V4.1 Flash and GLM-5.3 both fell to 32 vs leader Claude Opus 5.5 at 58

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Top US open weight Gemma 4 31B at 1452 Arena is only 36 Elo behind top Chinese Kimi K3 at 1488 – a 55% vs 45% win rate, not a capability cliff

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License traps: Mistral Medium 3.5, Kimi K3 and Qwen3.8 Max all carry $20-50M revenue gates for MaaS – you can use at work but not resell as online service without separate license

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🇺🇸 Gemini10 places · 2 notes

The same question, answered by Gemini alone. The final list above combines all the AIs.

  1. 1
    DeepSeek-V3 (China, 2024–2026)

    Delivers 98% of closed frontier intelligence under an unencumbered MIT license with zero commercial user caps.

    Source ↗
  2. 2
    Qwen 2.5 72B Instruct (Alibaba, China)

    The open-source enterprise workhorse: true Apache 2.0 freedom that outperforms 400B-class models on price-to-accuracy ratios.

    Source ↗
  3. 3
    Qwen 2.5 Coder 32B Instruct (Alibaba, China)

    Fits inside a single workstation GPU while rivaling GPT-4o in software engineering under unconditional Apache 2.0 terms.

    Source ↗
  4. 4
    Llama 3.3 70B Instruct (Meta, USA)

    Matches the intelligence of early 405B models at one-sixth the inference cost, but locks operators under Meta's 700-million-user ceiling.

  5. 5
    Llama 3.1 405B Instruct (Meta, USA)

    The smartest weights ever released by an American tech titan, encumbered by an 8-GPU 80GB VRAM footprint and Meta's commercial rights gate.

  6. 6
    Mistral Large 4 (Mistral AI, France, 2026)

    A 1-trillion MoE heavyweight that ranks 8th on Artificial Analysis, but forbids selling model access as an online service without a paid commercial license.

    Source ↗
  7. 7
    DeepSeek-Coder-V2.5 (China)

    A coding specialist scoring 80+ on HumanEval while retaining unrestricted MIT re-licensing for enterprise developer platforms.

    Source ↗
  8. 8
    Gemma 2 27B (Google, USA)

    Punches far above its weight class in math and reasoning, but binds developers to Google's strict acceptable use and redistribution terms.

    Source ↗
  9. 9
    GLM-4 9B / GLM-5.3 Max (Zhipu AI, China)

    Bilingual benchmark darling that delivers fluid function-calling, though international SaaS providers navigate nuanced Chinese export regulations.

    Source ↗
  10. 10
    Command R+ (Cohere, Canada)

    A corporate multilingual titan with near-zero legal freedom for production unless you pay Cohere upfront enterprise fees.

What Gemini noticed

The 'Open-Source' paradox: While Mistral Large 4 ranks 8th in independent intelligence with a score of 38, its Ecosystem License expressly forbids hosting it as an online service for third parties without a commercial agreement, making Apache-2.0 models like Qwen 2.5 72B far more viable for commercial platforms.

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The Stanford CRFM Foundation Model Transparency Index reveals that Western hyperscalers and European labs increasingly attach behavioral, user-cap, and anti-resale riders, leaving Chinese open-weight labs as the primary source of unencumbered permissive licenses (MIT and Apache 2.0).

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🇨🇳 Wenxin10 places · 1 notes

The same question, answered by Wenxin alone. The final list above combines all the AIs.

  1. 1
    Meta Llama 4 Maverick 405B (2026)

    405B parameters, Apache 2.0 — the most free large model you can actually sell

  2. 2
    DeepSeek-V3.2 671B (2025/2026)

    671B MoE, MIT-style license — cheaper than a coffee run per million tokens

  3. 3
    Qwen3 235B-A22B MoE (Alibaba, 2026)

    235B total with 22B active — the best bang-per-token from China

    Source ↗
  4. 4
    Mistral Large 4 123B (Mistral AI, France, 2026)

    123B and top-10 smart — but you need a separate license to sell it online

    Source ↗
  5. 5
    Falcon 3 180B (TII, UAE, 2026)

    180B Apache 2.0 — the Gulf state's gift to the open-weight world

  6. 6
    Google Gemma 3 27B (2026)

    27B punches way above its weight — the laptop champion

    Source ↗
  7. 7
    NVIDIA Nemotron 4 340B (2025/2026)

    340B from the chip maker — built to sell you more GPUs

  8. 8
    Microsoft Phi-4 14B (2025/2026)

    14B from Redmond — the model that fits in your pocket

  9. 9
    Cohere Command R+ 104B (2025/2026)

    104B with built-in RAG — the enterprise workhorse with a catch

    Source ↗
  10. 10
    Stability AI Stable Code 3 15B (2026)

    15B code specialist — Apache 2.0 and free to ship

What Wenxin noticed

DeepSeek-V3.2 costs less than 1 per million tokens self-hosted, making it the cheapest top-tier open-weight model by a wide margin

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🇨🇳 DeepSeek10 places · 2 notes

The same question, answered by DeepSeek alone. The final list above combines all the AIs.

  1. 1
    DeepSeek V4.1 Flash 1017 (2026)

    A 32 on AA v5 with a plain MIT license — the rare case where the smartest released open model is also the freest.

    Source ↗
  2. 2
    GLM-5.3 (2026)

    Ties DeepSeek at 32 on AA v5, but its license story is messier: some sources call it MIT, others read a hosting restriction into the GLM-5.3 terms.

    Source ↗
  3. 3
    MiMo-V2.6-Pro (Xiaomi, 2026)

    A 30 on AA v5 with a full MIT license and API pricing at $0.435/$0.87 per million tokens — the cheapest genuinely free-to-resell model in the top tier.

    Source ↗
  4. 4
    Kimi K3 (Moonshot AI, 2026)

    The highest raw intelligence among models with published weights — but the license adds a $20M revenue trigger and a 100M-MAU cap, so “open” comes with a commercial door.

    Source ↗
  5. 5
    Muse Spark Open (Meta, 2026)

    A 25 on AA v5 and a strong #3 on finance-agent tasks, but the Muse Model License triggers a separate agreement at $20M revenue for MaaS resale.

  6. 6
    Mistral Large 4 (Le Chonk, 2026)

    Free to run inside your company with no revenue cap, but the moment you host it for customers you need a separate Mistral commercial license — the definition of “open in name, gated in service.”

    Source ↗
  7. 7
    Step 5 Preview (StepFun, 2026)

    A 29 on AA v5 and a 1M-token context, but the StepFun Model License 1.0 carries OEM and MaaS gates that make “open weights” a deployment-permission question.

  8. 8
    Qwen 3.8 Max (Alibaba, 2026)

    A 2.4T-parameter flagship with strong coding-agent scores, but the Qwen3.8-Max License introduces a $50M revenue gate for competing MaaS and coding-assistant businesses — a departure from Qwen’s Apache-2.0 past.

    Source ↗
  9. 9
    Nemotron 3 Ultra (NVIDIA, 2026)

    Only a 23 on AA v5, but the OpenMDW-1.1 license is genuinely permissive — weights, training data and recipes — making it the most transparent agent-capable model in the top ten.

  10. 10
    Gemma 4 (Google, 2026)

    The intelligence is modest (2B–31B sizes), but Apache 2.0 means no revenue cap, no MaaS trigger and no permission email — the cleanest “do what you want” license on the list.

What DeepSeek noticed

The open-vs-closed intelligence gap widened from 3 points to 12 points after AA v5 — and to 26 points if you count only models with actually published weights, because AA’s headline 12-point figure includes Kimi K3.1 Pro, whose weights are still pending.

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Mistral Large 4’s license has no revenue threshold and no user cap — a genuine improvement over Meta’s $20M MaaS trigger — but the MaaS clause means a hosting startup still needs Mistral’s permission before selling access.

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🇨🇳 Qwen10 places · 3 notes

The same question, answered by Qwen alone. The final list above combines all the AIs.

  1. 1
    DeepSeek V4.1

    The open-weight king that actually lets you sell it without a permission slip.

    Source ↗
  2. 2
    Qwen 3.8-2.4T

    A trillion-parameter monster that only asks for a separate contract if you're already making $50 million a year.

    Source ↗
  3. 3
    Llama 3.3 405B

    The 700-million-user threshold means almost everyone on Earth can run it commercially for free.

  4. 4
    Mistral Large 4

    Europe's new 1T-parameter champion scores high on benchmarks but hides a 'MaaS tax' in its fine print.

    Source ↗
  5. 5
    Kimi K3.1 Pro

    Moonshot's cybersecurity specialist dominates real-world hacking benchmarks while keeping its core weights free to deploy.

    Source ↗
  6. 6
    Gemma 3 27B

    Google's compact powerhouse punches far above its weight on LMArena without any commercial usage caps.

    Source ↗
  7. 7
    Yi-Lightning

    01.AI's budget-friendly speedster delivers top-tier coding agents at a fraction of the API cost of US rivals.

    Source ↗
  8. 8
    Phi-4

    Microsoft's math-and-science prodigy runs on a single consumer GPU and carries a research-friendly license.

    Source ↗
  9. 9
    Falcon 3 40B

    The UAE's flagship model keeps its weights wide open under Apache 2.0, making it the default choice for sovereign AI clouds.

    Source ↗
  10. 10
    InternLM 3

    Shanghai AI Lab's latest release pairs deep reasoning with a fully open license for enterprise orchestration.

    Source ↗

What Qwen noticed

The 'open-weight' label is fracturing: models like Mistral Large 4 and Qwen 3.8 now require separate commercial contracts for Model-as-a-Service (MaaS) providers, redefining what 'free' means.

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DeepSeek's switch to the MIT License for its V3/V4 series has made it the undisputed safe harbor for enterprises that refuse to negotiate custom AI contracts.

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Stanford's Foundation Model Transparency Index dropped from 58 to 40 in twelve months, showing that as models get smarter, their developers get more secretive about training data.

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