Model comparison
DeepSeek-V3.2-Exp vs Mistral Small 3
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 31.2 on the Noometry Index. Mistral Small 3 costs 5.0× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 21 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Mistral Small 3 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.4 to 32.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 6.7% for Mistral Small 3.
- Mistral Small 3 is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 33K.
Side by side
| DeepSeek-V3.2-Exp | Mistral Small 3 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 31.2 |
| Released | 2025-09-29 | 2025-01-30 |
| Weights | Open | Open |
| Context window | 164K | 33K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.26 | $0.05 |
| Output $ / M tokens | $0.38 | $0.08 |
| Results tracked | 49 | 24 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Mistral Small 3: 36.5 (#207)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| LMArena Coding | 1454 | 1246 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Instruct | — | 45.3% |
| BigCodeBench Complete | — | 50.4% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Mistral Small 3: —
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.1 (#208), Mistral Small 3: 18.9 (#273)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1434 | 1233 |
| Epoch Capabilities Index | 146.27 | 127.07 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Mistral Small 3: 16.3 (#295)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 6.7% |
| LMArena Math | 1435 | 1240 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 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 3: 25.1 (#263)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| GPQA Diamond | 83.4% | 47.3% |
| LMArena Expert | 1436 | 1202 |
| Confabulations | — | 25.2% |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Mistral Small 3: 37.3 (#236)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| LMArena Non-English | 1409 | 1198 |
| LMArena Chinese | 1461 | 1204 |
| LMArena French | 1433 | 1203 |
| LMArena German | 1440 | 1211 |
| LMArena Japanese | 1374 | 1111 |
| LMArena Korean | 1371 | 1188 |
| LMArena Russian | 1424 | 1216 |
| LMArena Spanish | 1440 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Mistral Small 3: 63.7 (#229)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1214 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Mistral Small 3: 37.8 (#211)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| LMArena Longer Query | 1428 | 1246 |
| 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 3: 32.2 (#280)
| Benchmark | DeepSeek-V3.2-Exp | Mistral Small 3 |
|---|---|---|
| LMArena Text | 1425 | 1234 |
| LMArena Creative Writing | 1403 | 1195 |
| EQ-Bench Creative Writing | 1515 | 707 |
| LMArena Multi-Turn | 1427 | 1217 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Mistral Small 3?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 31.2 on the Noometry Index. Mistral Small 3 costs 5.0× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or Mistral Small 3?
Mistral Small 3 is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or Mistral Small 3 better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 33K.
How many benchmarks do DeepSeek-V3.2-Exp and Mistral Small 3 share?
21 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mistral Small 3 has 24.