Model comparison
Gemini 2.5 Flash-Lite vs Mistral Large
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 37.0 to 31.9 on the Noometry Index.
Last verified . 28 shared benchmarks.
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Mistral AISummary
- They share 28 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 7 categories and Mistral Large in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Gemini 2.5 Flash-Lite leads 38.0 to 18.2.
- The biggest single-benchmark swing is Omni-MATH: 48% for Gemini 2.5 Flash-Lite and 28.1% for Mistral Large.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $6 for Mistral Large.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | Mistral Large | |
|---|---|---|
| Provider | Mistral AI | |
| Noometry Index | 37.0 | 31.9 |
| Released | 2025-06-17 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.10 | $2 |
| Output $ / M tokens | $0.40 | $6 |
| Results tracked | 33 | 51 |
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Category by category
Coding Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.5 (#173), Mistral Large: 34.3 (#240)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Coding | 1373 | 1277 |
| ALE-Bench | 325.9 | 264.7 |
| SciCode | — | 36.2% |
| WeirdML | 35.2% | — |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Too close to call
Gemini 2.5 Flash-Lite: 28.0 (#96), Mistral Large: 28.6 (#89)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | 38.4% |
Reasoning Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 22.2 (#205), Mistral Large: 15.8 (#310)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1377 | 1257 |
| DTBench | 62.8% | 65.1% |
| LMCA | 18.1% | 16.7% |
| Epoch Capabilities Index | 133.94 | 128.52 |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 40.5% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| LiveBench Data Analysis | — | 50.1% |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.0 (#144), Mistral Large: 18.2 (#291)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| Omni-MATH | 48% | 28.1% |
| LMArena Math | 1373 | 1262 |
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 32.5 (#210), Mistral Large: 30.1 (#230)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| MMLU-Pro | 53.7% | 59.9% |
| Vectara Hallucination Rate | 3.3% | 4.5% |
| GPQA (HELM) | 30.9% | 43.5% |
| LMArena Expert | 1373 | 1232 |
| GPQA Diamond | — | 51.3% |
| Confabulations | — | 21.4% |
| MMLU | — | 80% |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 29.1 (#114), Mistral Large: —
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.3 (#134), Mistral Large: 40.0 (#219)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Non-English | 1369 | 1237 |
| LMArena Chinese | 1404 | 1240 |
| LMArena French | 1388 | 1325 |
| LMArena German | 1389 | 1254 |
| LMArena Japanese | 1359 | 1188 |
| LMArena Korean | 1360 | 1202 |
| LMArena Russian | 1373 | 1257 |
| LMArena Spanish | 1396 | 1268 |
Instruction Following Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 70.0 (#168), Mistral Large: 67.9 (#191)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| IFEval | 81% | 87.7% |
| LMArena Instruction Following | 1367 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
Long Context Mistral Large leads
Gemini 2.5 Flash-Lite: 33.3 (#262), Mistral Large: 38.3 (#199)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1373 | 1261 |
| Fiction.LiveBench | 47.2% | — |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 56.8 (#135), Mistral Large: 40.7 (#242)
| Benchmark | Gemini 2.5 Flash-Lite | Mistral Large |
|---|---|---|
| LMArena Text | 1379 | 1266 |
| LMArena Creative Writing | 1367 | 1243 |
| WildBench | 81.8% | 80.1% |
| LMArena Multi-Turn | 1366 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than Mistral Large?
Gemini 2.5 Flash-Lite is the stronger model overall, scoring 37.0 to 31.9 on the Noometry Index.
Which is cheaper, Gemini 2.5 Flash-Lite or Mistral Large?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Mistral Large lists at $2 and $6.
Is Gemini 2.5 Flash-Lite or Mistral Large better for coding?
Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.5 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 131K.
How many benchmarks do Gemini 2.5 Flash-Lite and Mistral Large share?
28 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and Mistral Large has 51.