Mistral Medium 3.5 leads on the shared-benchmark average
Ahead on 3 of 8 shared benchmarks, averaging 0.1 points higher
Summarised only from the 8 percentage-scale benchmarks scored by every selected model; details are below.
“Best available” takes each model’s best recorded non-parallel result in the metric’s stated direction per benchmark, so it may combine modes into a virtual configuration that does not exist. Read it with the mode breakdown.

Mistral Medium 3.5
MistralAI

Gemma 4 31B
DeepMind
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
“Best available” picks the best non-parallel result in the metric’s stated direction separately for each benchmark. The resulting series can combine several reasoning levels and is not one reproducible runtime configuration. Choose a mode filter to compare like-for-like runs.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: 长上下文能力 +22.6 / Relative gap: 综合评估 -6.6
Relative edge: 综合评估 +6.6 / Relative gap: 长上下文能力 -22.6
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
13 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | Mistral Medium 3.5 | Gemma 4 31B |
|---|---|---|
13.80Thinking Enabled | 26.50Thinking Enabled | Tools | |
74.80Thinking Enabled | 84.30Thinking Enabled | |
33.30Thinking Enabled | Tools | 36.40Thinking Enabled | Tools | |
94.20Thinking Enabled | Tools | 65.50Standard Mode | Tools | |
15.10Thinking Enabled | Tools | 14.80Thinking Enabled | Tools | |
68.80Thinking Enabled | 75.60Thinking Enabled | |
69.30Thinking Enabled | 46.70Standard Mode | |
50.60Thinking Enabled | Tools | 43.40Thinking Enabled | Tools | |
875.00Thinking Enabled | Tools | 691.00Standard Mode | Tools | |
69.10Thinking Enabled | Tools | 47.23Thinking Enabled | Tools | |
2.80Thinking Enabled | 6.00Thinking Enabled | |
64.90Thinking Enabled | 76.90Thinking Level · High | |
39.58Thinking Enabled | 45.50Thinking Enabled |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | Mistral Medium 3.5MistralAI | Gemma 4 31BDeepMind |
|---|---|---|
Core specsRelease | 2026-05-01 | 2026-04-02 |
Context length | — | 256K |
Total parameters | — | 30.7B |
Max output length | Not provided | 32,768 tokens |
Architecture | Undisclosed | Dense |
Runtime modes | No runtime mode data | 关闭开启 |
Availability & licensingCode availability | Not public | Available · Apache 2.0 |
Weight availability | Not public | Available · Apache 2.0 |
Use & commercial terms | Official service only; subject to provider terms | 免费商用授权 |
Local deploymentWeights | Not provided | Hugging Face |
API modality supportText Input/Output | Input:NoOutput:No | Input:YesOutput:Yes |
Image Input/Output | Input:NoOutput:No | Input:YesOutput:No |
ResourcesPaper / report | Not provided | Gemma 4 Model Card |
DataLearner blog | Not provided | Google Gemma 4 正式开源:Apache 2.0 协议、手机端可运行、原生支持多模态和 Agent 工作流 |