The best free AI models, ranked by real votes

Only models you can use without paying: free online, or downloaded to your own GPU. Scores come from millions of blind human votes on Arena, updated every night.

226 of 416 models in Arena's Chat ranking are free to use. Votes as of 15 Oct 2026. Best free: mimo-v2.6-pro, #11 overall.

Full free ranking

Free rankModelArena scoreSizeDownload4-bit needsQuality at 4-bitSpeed on RTX 4090Use free online
1 mimo-v2.6-pro
xiaomi
1491
#11 of 416
1024.2 B
44.9 B active
Yes
MIT
622 GB +1.2%
very small loss
does not fit —
2 Kimi K3 (Max)
moonshot
1475
#25 of 416
2779.9 B
105.8 B active
Yes
Kimi K3 license
1686 GB +1.2%
very small loss
does not fit Lab chat
3 glm-5.3-max
zai
1471
#27 of 416
753.3 B
51.2 B active
Yes
MIT
458 GB +1.2%
very small loss
does not fit Lab chat
4 glm-5.3-flash
zai
1471
#29 of 416
321.3 B
25.4 B active
Yes
MIT
195 GB +1.2%
very small loss
does not fit Lab chat
5 glm-5.2-max
zai
1470
#31 of 416
753.3 B
51.2 B active
Yes
MIT
458 GB +1.2%
very small loss
does not fit Lab chat
6 mimo-v2.5-pro
xiaomi
1465
#37 of 416
1023.2 B
43.8 B active
Yes
MIT
621 GB +1.2%
very small loss
does not fit —
7 deepseek-v4.1-flash-max
deepseek
1463
#38 of 416
763.2 B
228.1 B active
Yes
MIT
464 GB +1.2%
very small loss
does not fit Lab chat
8 glm-5.1
zai
1462
#39 of 416
753.9 B
51.7 B active
Yes
MIT
458 GB +1.2%
very small loss
does not fit Lab chat
9 kimi-k2.6
moonshot
1455
#44 of 416
1026.9 B
33.3 B active
Yes
Modified MIT
623 GB +1.2%
very small loss
does not fit Lab chat
10 mimo-v2.6-flash
xiaomi
1455
#45 of 416
310.8 B
17.4 B active
Yes
MIT
189 GB +1.2%
very small loss
does not fit —
11 deepseek-v4-pro
deepseek
1451
#49 of 416
1598.8 B
75.6 B active
Yes
MIT
970 GB +1.2%
very small loss
does not fit Lab chat
12 glm-5
zai
1446
#60 of 416
753.9 B
51.8 B active
Yes
MIT
458 GB +1.2%
very small loss
does not fit Lab chat
13 deepseek-v4-pro-high-20260813
deepseek
1446
#61 of 416
— Yes
MIT
— — — Lab chat
14 kimi-k2.5-thinking
moonshot
1445
#62 of 416
— Yes
Modified MIT
— — — Lab chat
15 deepseek-v4-pro-high-preview
deepseek
1445
#63 of 416
— Yes
MIT
— — — Lab chat
16 nvidia-nemotron-3-ultra-550b-a55b-nvfp4
nvidia
1445
#64 of 416
560.5 B
55 B active
Yes
OpenMDW-1.1
340 GB +1.2%
very small loss
does not fit Lab chat
17 gemma-4-31b
google
1443
#67 of 416
31.3 B Yes
Apache 2.0
22 GB +1.8%
small loss
33 tok/s Free API · try itLab chat
18 inkling
thinky
1441
#74 of 416
— Yes
Apache 2.0
— — — Free API · try it
19 qwen3.8-27b
alibaba
1441
#75 of 416
27.8 B Yes
Apache 2.0
18 GB +1.9%
small loss
41 tok/s Lab chat
20 glm-4.6
zai
1440
#76 of 416
356.8 B
37.6 B active
Yes
MIT
220 GB +1.2%
very small loss
does not fit Lab chat
21 hy3
tencent
1438
#80 of 416
298.8 B
24.4 B active
Yes
Apache 2.0
184 GB +1.2%
very small loss
does not fit Lab chat
22 qwen3.5-397b-a17b
alibaba
1438
#81 of 416
403.4 B
17 B active
Yes
Apache 2.0
245 GB +1.2%
very small loss
does not fit Lab chat
23 glm-4.7
zai
1435
#87 of 416
358.3 B
39.1 B active
Yes
MIT
221 GB +1.2%
very small loss
does not fit Lab chat
24 gemma-4-26b-a4b
google
1434
#90 of 416
25.8 B
4 B active
Yes
Apache 2.0
17 GB +1.9%
small loss
238 tok/s Free API · try itLab chat
25 minimax-m3
minimax
1433
#91 of 416
427 B
26.8 B active
Yes
MiniMax Community License
260 GB +1.2%
very small loss
does not fit Lab chat
26 deepseek-v4-flash
deepseek
1432
#92 of 416
290.9 B
20.4 B active
Yes
MIT
178 GB +1.2%
very small loss
does not fit Lab chat
27 glm-4.5
zai
1430
#95 of 416
358.3 B
39.1 B active
Yes
MIT
221 GB +1.2%
very small loss
does not fit Lab chat
28 mistral-large-3
mistral
1428
#98 of 416
— Yes
Apache 2.0
— — — —
29 mimo-v2.5
xiaomi
1428
#100 of 416
310.8 B
17.4 B active
Yes
MIT
189 GB +1.2%
very small loss
does not fit —
30 deepseek-r1-0528
deepseek
1428
#101 of 416
684.5 B
51 B active
Yes
MIT
416 GB +1.2%
very small loss
does not fit Lab chat
31 deepseek-v3.2-exp-thinking
deepseek
1425
#104 of 416
685.4 B
51.9 B active
Yes
MIT
417 GB +1.2%
very small loss
does not fit Lab chat
32 deepseek-v3.2
deepseek
1424
#105 of 416
685.4 B
51.9 B active
Yes
MIT
417 GB +1.2%
very small loss
does not fit Lab chat
33 deepseek-v4-flash-high-preview
deepseek
1424
#106 of 416
— Yes
MIT
— — — Lab chat
34 longcat-flash-chat
meituan
1423
#109 of 416
561.9 B
33.4 B active
Yes
MIT
341 GB +1.2%
very small loss
does not fit —
35 deepseek-v3.2-exp
deepseek
1422
#111 of 416
685.4 B
51.9 B active
Yes
MIT
417 GB +1.2%
very small loss
does not fit Lab chat
36 mistral-medium-3.5
mistral
1421
#112 of 416
— Yes
Modified MIT
— — — —
37 kimi-k2.5-instant
moonshot
1420
#113 of 416
— Yes
Modified MIT
— — — Lab chat
38 qwen3-vl-235b-a22b-instruct
alibaba
1420
#114 of 416
235.7 B
22 B active
Yes
Apache 2.0
145 GB +1.2%
very small loss
does not fit Lab chat
39 deepseek-v3.2-thinking
deepseek
1420
#115 of 416
685.4 B
51.9 B active
Yes
MIT
417 GB +1.2%
very small loss
does not fit Lab chat
40 deepseek-v3.1
deepseek
1420
#116 of 416
684.5 B
51.1 B active
Yes
MIT
416 GB +1.2%
very small loss
does not fit Lab chat
41 qwen3-235b-a22b-instruct-2507
alibaba
1419
#119 of 416
235.1 B
22 B active
Yes
Apache 2.0
145 GB +1.2%
very small loss
does not fit Lab chat
42 deepseek-v3.1-terminus-thinking
deepseek
1419
#120 of 416
— Yes
MIT
— — — Lab chat
43 qwen3.5-122b-a10b
alibaba
1417
#123 of 416
125.1 B
10 B active
Yes
Apache 2.0
77 GB +1.2%
very small loss
does not fit Lab chat
44 deepseek-v3.1-terminus
deepseek
1417
#125 of 416
— Yes
MIT
— — — Lab chat
45 qwen3-next-80b-a3b-instruct
alibaba
1417
#126 of 416
— Yes
Apache 2.0
— — — Lab chat
46 deepseek-v3.1-thinking
deepseek
1417
#128 of 416
— Yes
MIT
— — — Lab chat
47 qwen3-235b-a22b-thinking-2507
alibaba
1415
#131 of 416
— Yes
Apache 2.0
— — — Lab chat
48 kimi-k2-thinking-turbo
moonshot
1414
#133 of 416
— Yes
Modified MIT
— — — Lab chat
49 Inkling Small
thinky
1413
#135 of 416
— Yes
Apache 2.0
— — — Free API · try it
50 mimo-v2-flash (non-thinking)
xiaomi
1411
#139 of 416
— Yes
MIT
— — — —
51 nvidia-nemotron-3.5-super-vl-120b-a12b-nvfp4
nvidia
1409
#141 of 416
— Yes
OpenMDW-1.1
— — — Lab chat
52 qwen3.5-27b
alibaba
1409
#142 of 416
27.8 B Yes
Apache 2.0
18 GB +1.9%
small loss
41 tok/s Lab chat
53 minimax-m2.7
minimax
1405
#148 of 416
— Yes
Modified MIT
— — — Lab chat
54 step-3.5-flash
stepfun
1403
#150 of 416
— Yes
Apache 2.0
— — — —
55 hunyuan-hy3-preview
tencent
1402
#151 of 416
— Yes
tencent-hunyuan-community
— — — Lab chat
56 qwen3-vl-235b-a22b-thinking
alibaba
1401
#152 of 416
235.7 B
22 B active
Yes
Apache 2.0
145 GB +1.2%
very small loss
does not fit Lab chat
57 qwen3.5-35b-a3b
alibaba
1395
#161 of 416
— Yes
Apache 2.0
— — — Lab chat
58 mimo-v2-flash (thinking)
xiaomi
1395
#162 of 416
— Yes
MIT
— — — —
59 qwen3-235b-a22b-no-thinking
alibaba
1393
#164 of 416
— Yes
Apache 2.0
— — — Lab chat
60 minimax-m2.1-preview
minimax
1392
#165 of 416
— Yes
MIT
— — — Lab chat

Size: parameters (and active parameters for mixture-of-experts). 4-bit needs: GPU memory at Q4_K_M with 8K of context. Quality at 4-bit: perplexity increase against the full model, from llama.cpp tests scaled by size. Speed: estimate on one RTX 4090 (24 GB) with 64 GB of DDR5, at 4-bit. Simulate your own hardware.

Score against size

Each dot is an open model; up is better, left is smaller. The line joins the models that beat every smaller one. Parameters on a log scale.

Top free models over the last six months

Arena score from 17 Apr 2026 to 15 Oct 2026.

Who beats whom

Model A ↓ · Model B →Gemini 4 Argon (High) (paid)mimo-v2.6-proKimi K3 (Max)glm-5.3-maxglm-5.3-flashglm-5.2-maxmimo-v2.5-prodeepseek-v4.1-flash-maxglm-5.1kimi-k2.6mimo-v2.6-flashdeepseek-v4-proglm-5
Gemini 4 Argon (High) paid0.560.580.590.590.590.600.600.600.610.610.610.62
mimo-v2.6-pro0.440.520.530.530.530.540.540.540.550.550.560.56
Kimi K3 (Max)0.420.480.510.510.510.510.520.520.530.530.530.54
glm-5.3-max0.410.470.490.500.500.510.510.510.520.520.530.54
glm-5.3-flash0.410.470.490.500.500.510.510.510.520.520.530.54
glm-5.2-max0.410.470.490.500.500.510.510.510.520.520.530.53
mimo-v2.5-pro0.400.460.490.490.490.490.500.500.510.510.520.53
deepseek-v4.1-flash-max0.400.460.480.490.490.490.500.500.510.510.520.52
glm-5.10.400.460.480.490.490.490.500.500.510.510.520.52
kimi-k2.60.390.450.470.480.480.480.490.490.490.500.510.51
mimo-v2.6-flash0.390.450.470.480.480.480.490.490.490.500.510.51
deepseek-v4-pro0.390.440.470.470.470.470.480.480.480.490.490.51
glm-50.380.440.460.460.460.470.470.480.480.490.490.49
A usually loses (down to 0.38)Even (0.50)A usually wins (up to 0.62)

Each cell: how often the model on the row is expected to beat the model in the column, from their Arena scores (1 / (1 + 10^((B − A) / 400)), the Elo formula behind Arena's ratings). Arena does not publish every single battle, so these are expected rates, not counted ones.

Small models, no Arena votes yet

Official models from the labs' Hugging Face pages, 0.5 to 16 billion parameters. Arena has not voted on them, so they are ordered by downloads, which shows popularity, not quality. They run on 4 to 12 GB of GPU memory.

ModelDownloads/monthSizeRun at home
Qwen3-0.6B
Qwen
29.5M0.8 B ~3 GB at 4-bit · simulate
Qwen3-4B
Qwen
11.8M4 B ~5 GB at 4-bit · simulate
Qwen3-8B
Qwen
8.9M8.2 B ~7 GB at 4-bit · simulate
Qwen3-14B
Qwen
8.4M14.8 B ~12 GB at 4-bit · simulate
Qwen3.5-4B
Qwen
7.8M4.7 B ~5 GB at 4-bit · simulate
Qwen3.5-9B
Qwen
7.5M9.7 B ~8 GB at 4-bit · simulate
gemma-3-1b-it
google · license to accept
1.6M1 B ~3 GB at 4-bit · simulate
NVIDIA-Nemotron-3-Nano-4B-BF16
nvidia
1.6M4 B ~5 GB at 4-bit · simulate
SmolVLM2-500M-Video-Instruct
HuggingFaceTB
1.4M0.5 B ~2 GB at 4-bit · simulate
MiniCPM5-2B
openbmb
1.3M2.5 B ~4 GB at 4-bit · simulate
DeepSeek-R1-Distill-Qwen-1.5B
deepseek-ai
1.3M1.8 B ~3 GB at 4-bit · simulate
gemma-3-4b-it
google · license to accept
1.1M4.3 B ~5 GB at 4-bit · simulate
SmolLM3-3B
HuggingFaceTB
749K3.1 B ~4 GB at 4-bit · simulate
granite-4.1-3b
ibm-granite
648K3.4 B ~4 GB at 4-bit · simulate
Apertus-v1.5-8B
swiss-ai · license to accept
556K8.9 B ~8 GB at 4-bit · simulate
Olmo-3-7B-Think
allenai
489K7.3 B ~7 GB at 4-bit · simulate
DeepSeek-R1-0528-Qwen3-8B
deepseek-ai
445K8.2 B ~7 GB at 4-bit · simulate
Apertus-8B-Instruct-2509
swiss-ai
434K8.1 B ~7 GB at 4-bit · simulate
Olmo-3-7B-Instruct
allenai
400K7.3 B ~7 GB at 4-bit · simulate
DeepSeek-R1-Distill-Qwen-7B
deepseek-ai
386K7.6 B ~7 GB at 4-bit · simulate

New, waiting for Arena votes

Released in the last 60 days by the labs' official Hugging Face pages, or new free models on OpenRouter, that Arena has not voted on yet. We check every night; once Arena votes, a model moves into the ranking above.

ReleasedModelTypeSize4-bit needsUse it free
17 Oct 2026 DeepSeek-V4.1-Flash-1017
deepseek-ai
Chat, Vision 763.2 B
228.1 B active
511 GB Download
16 Oct 2026 NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16
nvidia
Chat 4 B 5 GB Download
15 Oct 2026 Hy-Memory-30B-A3B
tencent
Chat 30.1 B
3 B active
22 GB Download
14 Oct 2026 granite-switch-4.1-8b
ibm-granite
Chat 8.8 B 8 GB Download
14 Oct 2026 granite-switch-4.1-30b
ibm-granite
Chat 30 B 22 GB Download
14 Oct 2026 flux-3-image-klein-pup
black-forest-labs
Image 9.4 B 8 GB Download
13 Oct 2026 Ling-3.1-flash
inclusionAI
Chat 572.1 B
33.1 B active
383 GB Download
13 Oct 2026 flux-3-image-klein
black-forest-labs
Image edit 5.5 B 6 GB Download
12 Oct 2026 NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16
nvidia
Chat, Vision 124.5 B
12 B active
85 GB Free API · try itDownload
12 Oct 2026 LFM2.5-24B-A2B
LiquidAI
Chat 24 B
2 B active
18 GB Download
9 Oct 2026 NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16
nvidia
Chat, Vision 74.9 B
9 B active
52 GB Download
7 Oct 2026 NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB
nvidia
Chat, Vision 22.4 B
9 B active
17 GB Download
7 Oct 2026 MiniCPM-V-4.7-35B-A3B
openbmb
Chat, Vision 35.2 B
3 B active
25 GB Download
6 Oct 2026 MiniCPM-V-4.7-1B
openbmb
Chat, Vision 1.6 B 3 GB Download
5 Oct 2026 d1-omni-600M
LiquidAI
Chat, Vision 0.6 B 2 GB Download
5 Oct 2026 d1-3B
LiquidAI
Chat, Vision 3.1 B 4 GB Download
29 Sept 2026 AesCode-32B
microsoft
Chat, Vision, Coding 33.4 B 24 GB Download
29 Sept 2026 AesCode-8B
microsoft
Chat, Vision, Coding 8.8 B 8 GB Download
17 Sept 2026 Ming-Image-0.1-Design
inclusionAI
Image 6.2 B 6 GB Download
9 Sept 2026 Kandinsky-6.0-Lite-distill-5s-Diffusers
kandinskylab
Video 3.2 B 4 GB Download
9 Sept 2026 Kandinsky-6.0-Pro-distill-5s-Diffusers
kandinskylab
Video 30.1 B 22 GB Download
6 Sept 2026 LLaDA-UI
inclusionAI
Chat, Vision 16.9 B 13 GB Download
2 Sept 2026 tiny-aya-l2-thinker
CohereLabs
Chat 3.4 B 4 GB Download
27 Aug 2026 Qwen-Drive-1.0-4B
Qwen
Chat, Vision 4.5 B 5 GB Download
27 Aug 2026 ContextPilot-14B
tencent
Chat 14.8 B 12 GB Download
free API XingChen: Xing4.0 29B A4B
xingchen-agi
Chat — — Free API · try it
free API Dots Studio: Dots3-Note Preview
dots-studio
Chat — — Free API · try it
free API LiquidAI: LFM2.5-2.6B
liquid
Chat — — Free API · try it
free API NVIDIA: Nemotron 3.5 Lightning
nvidia
Chat — — Free API · try it
free API Poolside: Laguna S 2.1
poolside
Chat — — Free API · try it
free API Poolside: Laguna XS 2.1
poolside
Chat — — Free API · try it
free API Cohere: North Mini Code
cohere
Chat — — Free API · try it
free API NVIDIA: Nemotron 3 Ultra
nvidia
Chat — — Free API · try it
free API NVIDIA: Nemotron 3 Nano Omni
nvidia
Chat — — Free API · try it
free API NVIDIA: Nemotron 3 Super
nvidia
Chat — — Free API · try it

Frequently asked questions

What about brand-new models?

Arena takes a few days or weeks to collect enough votes. Meanwhile, new models from the labs’ official pages appear in “New, waiting for Arena votes”, with their size, memory needs and speed. When Arena votes, they move into the ranking automatically.

What counts as a free model?

A model with open weights that you can download and run yourself, or a model you can call through a free API today (we check OpenRouter every night). Paid-only models are left out of the rankings, but we show the best paid score as a reference line.

Where do the scores come from?

From Arena (formerly LMSYS Chatbot Arena), where people compare two anonymous answers and vote for the better one. Arena publishes its leaderboard as an open dataset under the CC BY 4.0 license; we filter it to free models. We are not affiliated with Arena.

How often is it updated?

Every night. A scheduled job downloads the latest Arena data, the free model list from OpenRouter and model sizes from Hugging Face, then rebuilds the whole site, including the weekly report on the blog.

Can I run these models on my computer?

Open models, yes, if they fit in your GPU memory. The Run at home page estimates memory and speed for your exact card, from one to ten cards, using each model's real architecture.

Why do small models have no score?

Arena votes mostly on large models. We list the most popular small models from the labs' official Hugging Face pages separately and mark them “No Arena votes”, so you can still find something that runs on a 4 to 12 GB card.