[whatmodel.sh](../../README.md) / Mistral Medium 3.5 (128B) # Mistral Medium 3.5 (128B) Mistral · released 2026-04 · 128B params · modified-mit **score 64.2** ±0.8 · #153 of 732 · from 22 benchmark results across 5 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Mistral Medium 3.5 (128B) among every model on that task or test, worst to best. 
task                vs the others          rank  counts  tests
agent tasks         ▁▁▃▆█▄▅●▄▄▂▁     #96 of 313     30%      8
coding              ▁▁▁▃▅▇█●▅▄▃▁    #158 of 469     17%      3
chat & writing      ▁▁▁▃▆▄▆█▆●▇▃    #83 of 402*    7.0%      1
math                ▁▁▃▅█▆●█▄▂▁▁   #183 of 431*    8.3%      2
vision & documents  ▁▁▂▂▁▃▄▂●▇█▄    #81 of 153*    4.6%      1
agent tasks · #96 of 313 · 30% of its score Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.1](gpt-5-1.md), [o3-pro](o3-pro.md) here, though they rank higher overall. 
test                                     result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  -0.1265 IPS  ▁▁●▃▃▂█▁▂▂▁▁ beats 4/59   avg -0.379 IPS           18%  sharp
Terminal-Bench Hard                       33.3%  ▄········█●▄ beats 3/5    very hard · avg 4.6%    3.1%  sharp
APEX-Agents (pass@1)                       6.1%  ▄▄····●···█▄ beats 2/5    very hard · avg 1.9%    2.6%  too hard for it
MCP-Atlas (public set)                    30.7%  ▄●▄·······█▄ beats 1/5    hard · avg 19%          2.3%  at its level
EEGAgentBench overall (tool-based…        41.9%  ▂·▁▁··●▇█▂·▁ beats 4/25   hard · avg 28%          2.0%  blunt · at its level
SWE-bench Verified                        69.6%  ▄··········● beats 2/5    hard · avg 15%          1.9%  sharp · at its level · noisy
τ³-bench Banking                           5.8%  ●·▄▄······▄▄ beats 0/5    very hard · avg 5.3%    0.7%  blunt
τ³-bench telecom (tau2-bench ≥ v1…        91.4%  ▄·▄●▂··▂▂·▂▄ beats 6/13   medium · avg 64%           —  not counted yet: no other model in the …
coding · #158 of 469 · 17% of its score Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
Arena WebDev (Code Arena)           1264.0  ▁▁▃●▅█▃▆▅▅▁▂ beats 16/129  avg 981              12%  sharp
LiveCodeBench v6 (window unspecif…   84.9%  ▄······●▄▄·█ beats 1/5     medium · avg 50%    3.0%  typical
SWE-bench Verified                   69.6%  ▄··········● beats 2/5     hard · avg 15%      1.9%  sharp · at its level · noisy
chat & writing · #83 of 402 · 7.0% of its score Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Muse Glimmer 30B](muse-glimmer-30b.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1426.4  ▁▁▁▂▃▃▃▆▆●█▃ beats 281/385  avg 1260      7.0%  blunt
math · #183 of 431 · 8.3% of its score Beats [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall. Trails [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md) here, though they rank lower overall. 
test                result  vs the others in its run  difficulty        counts  why
AIME 2026            89.0%  ▄●··▄····█·▄ beats 1/5    medium · avg 65%    5.7%  sharp
HMMT February 2026   62.9%  ●▄▄········█ beats 1/5    hard · avg 31%      2.6%  sharp · at its level · noisy
vision & documents · #81 of 153 · 4.6% of its score Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-5.4 nano](gpt-5-4-nano.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Qwen3.5 9B](qwen3-5-9b.md), [GPT-4.1](gpt-4-1.md) here, though they rank lower overall. 
test                             result  vs the others in its run   difficulty  counts  why
Arena vision (style controlled)  1199.6  ▁▁▂▂▂▂▃▄●▅█▇ beats 73/152  avg 1061      4.6%  blunt
other tests (9) 
test                                result  vs the others in its run  difficulty            counts  why
GPQA Diamond                         77.5%  █···█●·█··██ beats 2/5    hard · avg 41%           15%  sharp · at its level
MMLU-Pro                             81.2%  ▄·▄●····▄··█ beats 2/5    medium · avg 55%        4.7%  typical
Humanity's Last Exam                 12.8%  █●······▄·▄▄ beats 2/5    very hard · avg 4.7%    4.5%  typical
IFBench (aggregation unspecified)    69.0%  ▄···▄●··▄··█ beats 2/5    medium · avg 43%        2.9%  at its level
Icelandic LLM Leaderboard average…   14.9%  ▁▁●▂▂▄▁▃▄▄▄█ beats 3/38   very hard · avg 14%     2.3%  at its level
MultiChallenge                       49.8%  █··▄·●·····█ beats 3/5    hard · avg 25%          2.3%  at its level
WMT25 Terminology Translation (te…   90.3%  ▂·▂··▂·▄▂·●▆ beats 9/12   medium · avg 63%        1.3%  noisy
WMT24++                              76.7%  ●·▃·▅▅▃···▃▃ beats 1/10   easy · avg 70%          0.6%  blunt
WMT26 General MT (GEMBA-ESA, LLM …   75.7%  ▄·▄▄▄·●██▄█▄ beats 4/12   medium · avg 65%        0.5%  blunt · at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Solar Open 2 Technical Report                  13   51% ▇▇▇▇▇▇
LMArena leaderboard dataset                     4   42% ▇▇▇▇▇
Cohere                                          3    2% ▇
Miðeind Icelandic LLM Leaderboard               1    2% ▇
EEGAgentBench: Benchmarking LLM Agents …        1    2% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 64.2
Licences: Solar Open 2 Technical Report ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Cohere ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
Not counted (1) 
source                  test                            score  why
Mistral AI vendor-rep…  τ³-bench telecom (tau2-bench …  91.4%  too few models have it yet
## How to run it ### Through an API api (Mistral AI) · $3.00/M blended · $1.5 in / $7.5 out per 1M tokens · [docs.mistral.ai](https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04) [price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens, standard uncached input and output. Product: La Plateforme chat completions. API model: mistral-medium-3-5. Settings: service tier=standard, batch=no, cache=uncached input. Additional/excluded charges: Tools, cache storage, priority service and other modalities excluded.. Currency: USD. · [context source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): provider maximum input tokens; not an intrinsic model maximum or legacy max\_tokens ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Mistral Medium 3.5 (128B) (ud-q2_k_xl, needs ~38.9 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF/tree/main)) ```sh llama cli -hf unsloth/Mistral-Medium-3.5-128B-GGUF:UD-Q2_K_XL ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf unsloth/Mistral-Medium-3.5-128B-GGUF:UD-Q2_K_XL ``` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Mistral Medium 3.5 (128B) (4bit, needs ~75.4 GB, Mac only, from [huggingface.co/froggeric](https://huggingface.co/froggeric/Mistral-Medium-3.5-128B-MLX-4bit/tree/648c1b42c13087814da310f9fcbebf3af55212b5)) ```sh lms get https://huggingface.co/froggeric/Mistral-Medium-3.5-128B-MLX-4bit -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: ud-q2_k_xl (~38.9 GB) `lms get https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF@UD-Q2_K_XL -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Mistral Medium 3.5 (128B) (Q4_K_M, needs ~77.3 GB, from [ollama.com](https://ollama.com/library/mistral-medium-3.5/tags)) ```sh ollama run mistral-medium-3.5:128b ``` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Mistral Medium 3.5 (128B) (4bit, needs ~75.4 GB, Mac only, from [huggingface.co/froggeric](https://huggingface.co/froggeric/Mistral-Medium-3.5-128B-MLX-4bit/tree/648c1b42c13087814da310f9fcbebf3af55212b5)) ```sh mlx_lm.chat --model froggeric/Mistral-Medium-3.5-128B-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model froggeric/Mistral-Medium-3.5-128B-MLX-4bit ``` Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) [interactive version](https://whatmodel.sh/model/mistral-medium-3-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)