Model comparison
DeepSeek-V3.2-Exp vs Mistral 7B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 22.9 on the Noometry Index.
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 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 7.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 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 8K.
Side by side
| DeepSeek-V3.2-Exp | Mistral 7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.9 | 22.9 |
| Released | 2025-09-29 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 164K | 8K |
| Max output | 66K | 8K |
| Input $ / M tokens | $0.26 | $0.25 |
| Output $ / M tokens | $0.38 | $0.25 |
| Results tracked | 48 | 37 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Mistral 7B: 25.9 (#321)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| LMArena Coding | 1454 | 1082 |
| 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 | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), Mistral 7B: —
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.0 (#185), Mistral 7B: 12.6 (#331)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1433 | 1067 |
| DTBench | 87.7% | 42.5% |
| Epoch Capabilities Index | 146.28 | 111.97 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Thematic Generalization | 65% | — |
| LMCA | 29.1% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), Mistral 7B: 7.6 (#319)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 0.3% |
| LMArena Math | 1435 | 1085 |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| MATH Level 5 | — | 3.7% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 54.4% |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Mistral 7B: 7.3 (#306)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| GPQA Diamond | 83.4% | 15.2% |
| LMArena Expert | 1436 | 1036 |
| Vectara Hallucination Rate | 5.3% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), Mistral 7B: 26.0 (#278)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1409 | 1012 |
| LMArena Chinese | 1461 | 1009 |
| LMArena French | 1435 | 1036 |
| LMArena German | 1440 | 987 |
| LMArena Japanese | 1377 | 878 |
| LMArena Russian | 1423 | 1018 |
| LMArena Spanish | 1441 | 1026 |
| LMArena Korean | 1372 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), Mistral 7B: 54.5 (#275)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1060 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Mistral 7B: 32.2 (#266)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1428 | 1060 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), Mistral 7B: 30.9 (#281)
| Benchmark | DeepSeek-V3.2-Exp | Mistral 7B |
|---|---|---|
| LMArena Text | 1425 | 1090 |
| LMArena Creative Writing | 1403 | 1068 |
| LMArena Multi-Turn | 1427 | 1062 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Mistral 7B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 22.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or Mistral 7B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 8K.
How many benchmarks do DeepSeek-V3.2-Exp and Mistral 7B share?
21 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Mistral 7B has 37.