Model comparison
DeepSeek-V3.2-Exp vs Magistral Medium
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 35.2 on the Noometry Index.
Last verified . 22 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 22 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Magistral Medium 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 33.5.
- The biggest single-benchmark swing is ARC-AGI-1: 57% for DeepSeek-V3.2-Exp and 6.1% for Magistral Medium.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $2 / $5 for Magistral Medium.
- Magistral Medium accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.2-Exp | Magistral Medium | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 44.3 | 35.2 |
| Released | 2025-09-29 | 2025-03-17 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 66K | 16K |
| Input $ / M tokens | $0.26 | $2 |
| Output $ / M tokens | $0.38 | $5 |
| Results tracked | 49 | 22 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Magistral Medium: 39.1 (#161)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| SciCode | 38.9% | 39.2% |
| LMArena Coding | 1454 | 1319 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Magistral Medium: —
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| 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), Magistral Medium: 8.6 (#348)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| ARC-AGI-2 | 4% | 0% |
| Kagi LLM Benchmark | 52.2% | 16.2% |
| ARC-AGI-1 | 57% | 6.1% |
| CritPt | 2.9% | 0.3% |
| LMArena Hard Prompts | 1434 | 1267 |
| NYT Connections (extended) | 36.7% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Magistral Medium: 35.1 (#189)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Math | 1435 | 1250 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| 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), Magistral Medium: 33.5 (#202)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Expert | 1436 | 1223 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Magistral Medium: 39.6 (#224)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Non-English | 1409 | 1232 |
| LMArena Chinese | 1461 | 1227 |
| LMArena French | 1433 | 1267 |
| LMArena German | 1440 | 1248 |
| LMArena Japanese | 1374 | 1175 |
| LMArena Korean | 1371 | 1125 |
| LMArena Russian | 1424 | 1224 |
| LMArena Spanish | 1440 | 1271 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Magistral Medium: 66.0 (#211)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Instruction Following | 1413 | 1254 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Magistral Medium: 39.3 (#183)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Longer Query | 1428 | 1295 |
| 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), Magistral Medium: 46.3 (#219)
| Benchmark | DeepSeek-V3.2-Exp | Magistral Medium |
|---|---|---|
| LMArena Text | 1425 | 1255 |
| LMArena Creative Writing | 1403 | 1245 |
| LMArena Multi-Turn | 1427 | 1275 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Magistral Medium?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 35.2 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Magistral Medium?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Magistral Medium lists at $2 and $5.
Is DeepSeek-V3.2-Exp or Magistral Medium better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Magistral Medium does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and Magistral Medium share?
22 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Magistral Medium has 22.