Model comparison
DeepSeek-V3.2-Exp vs Mistral Small
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.4 on the Noometry Index.
Last verified . 26 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 26 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Mistral Small in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.2-Exp leads 41.7 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 5.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- Mistral Small accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.2-Exp | Mistral Small | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 33.4 |
| Released | 2025-09-29 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 66K | 256K |
| Input $ / M tokens | $0.26 | $0.15 |
| Output $ / M tokens | $0.38 | $0.60 |
| Results tracked | 49 | 39 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Mistral Small: 34.0 (#247)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| SciCode | 38.9% | 26.5% |
| LMArena Coding | 1454 | 1362 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 32.7 (#59), Mistral Small: 28.1 (#93)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 37.1% |
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.1 (#208), Mistral Small: 19.8 (#250)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 37.8% |
| CritPt | 2.9% | 0% |
| LMArena Hard Prompts | 1434 | 1335 |
| DTBench | 87.7% | 70.9% |
| LMCA | 29.1% | 20.6% |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LiveBench Reasoning | — | 44.8% |
| LiveBench Data Analysis | — | 53.7% |
| Epoch Capabilities Index | 146.27 | — |
| LiveBench | — | 44% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Mistral Small: 16.4 (#293)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 5.8% |
| LMArena Math | 1435 | 1341 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Mistral Small: 31.0 (#222)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| GPQA Diamond | 83.4% | 47.5% |
| Vectara Hallucination Rate | 5.3% | 5.1% |
| LMArena Expert | 1436 | 1291 |
| MMLU | — | 68.7% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Mistral Small: 33.5 (#96)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Mistral Small: 45.5 (#169)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| LMArena Non-English | 1409 | 1315 |
| LMArena Chinese | 1461 | 1340 |
| LMArena French | 1433 | 1337 |
| LMArena German | 1440 | 1340 |
| LMArena Japanese | 1374 | 1275 |
| LMArena Korean | 1371 | 1259 |
| LMArena Russian | 1424 | 1324 |
| LMArena Spanish | 1440 | 1346 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Mistral Small: 66.4 (#209)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1413 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Mistral Small: 40.4 (#156)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1428 | 1327 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), Mistral Small: 52.5 (#171)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small |
|---|---|---|
| LMArena Text | 1425 | 1338 |
| LMArena Creative Writing | 1403 | 1305 |
| LMArena Multi-Turn | 1427 | 1344 |
| EQ-Bench Creative Writing | 1515 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Mistral Small?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.4 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or Mistral Small better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and Mistral Small share?
26 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mistral Small has 39.