Model comparison
DeepSeek-V3.2-Exp vs Mixtral 8x7B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 27.1 on the Noometry Index.
Last verified . 20 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 20 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 83.4% for DeepSeek-V3.2-Exp and 30.6% for Mixtral 8x7B.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 32K.
Side by side
| DeepSeek-V3.2-Exp | Mixtral 8x7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 27.1 |
| Released | 2025-09-29 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 164K | 32K |
| Max output | 66K | 32K |
| Input $ / M tokens | $0.26 | $0.70 |
| Output $ / M tokens | $0.38 | $0.70 |
| Results tracked | 49 | 38 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Mixtral 8x7B: 32.8 (#269)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1454 | 1126 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Mixtral 8x7B: —
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| 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), Mixtral 8x7B: 18.2 (#285)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1115 |
| DTBench | 87.7% | 49.6% |
| Epoch Capabilities Index | 146.27 | 118.47 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LMCA | 29.1% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Mixtral 8x7B: 18.8 (#289)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1435 | 1147 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 74.4% |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Mixtral 8x7B: 11.0 (#301)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 83.4% | 30.6% |
| LMArena Expert | 1436 | 1088 |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 5.3% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Mixtral 8x7B: 29.6 (#266)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1409 | 1077 |
| LMArena Chinese | 1461 | 1055 |
| LMArena French | 1433 | 1166 |
| LMArena German | 1440 | 1114 |
| LMArena Japanese | 1374 | 931 |
| LMArena Korean | 1371 | 968 |
| LMArena Russian | 1424 | 1090 |
| LMArena Spanish | 1440 | 1111 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Mixtral 8x7B: 51.0 (#297)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1109 |
| IFEval | — | 57.5% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Mixtral 8x7B: 33.4 (#260)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1428 | 1103 |
| 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), Mixtral 8x7B: 34.2 (#270)
| Benchmark | DeepSeek-V3.2-Exp | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1425 | 1132 |
| LMArena Creative Writing | 1403 | 1109 |
| LMArena Multi-Turn | 1427 | 1115 |
| EQ-Bench Creative Writing | 1515 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Mixtral 8x7B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 27.1 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Mixtral 8x7B?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.
Is DeepSeek-V3.2-Exp or Mixtral 8x7B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 32K.
How many benchmarks do DeepSeek-V3.2-Exp and Mixtral 8x7B share?
20 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mixtral 8x7B has 38.