Model comparison
DeepSeek V4 Flash vs Mistral Medium 3.5
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 40.2 on the Noometry Index.
Last verified . 20 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 20 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Mistral Medium 3.5 in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 17.3.
- The biggest single-benchmark swing is NYT Connections (extended): 89.6% for DeepSeek V4 Flash and 12.9% for Mistral Medium 3.5.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium 3.5.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 262K.
Side by side
| DeepSeek V4 Flash | Mistral Medium 3.5 | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 53.6 | 40.2 |
| Released | 2026-04-24 | — |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 393K | 210K |
| Input $ / M tokens | $0.15 | $1.50 |
| Output $ / M tokens | $0.60 | $7.50 |
| Results tracked | 41 | 22 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.9 (#59), Mistral Medium 3.5: 36.0 (#213)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena WebDev | 1582 | 1264 |
| LMArena Coding | 1457 | 1461 |
| FrontierCode | 18.8% | — |
| SciCode | 49.9% | — |
| WeirdML | 63% | — |
| ALE-Bench | 1,306 | — |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), Mistral Medium 3.5: 17.3 (#295)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 41.4% |
| NYT Connections (extended) | 89.6% | 12.9% |
| LMArena Hard Prompts | 1444 | 1436 |
| Epoch Capabilities Index | 154.49 | 141.35 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| ARC-AGI-1 | 89% | — |
| CritPt | 16.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), Mistral Medium 3.5: 39.1 (#113)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Math | 1427 | 1431 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| OTIS Mock AIME 2024-2025 | 94.4% | — |
| ProofBench | 56% | — |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), Mistral Medium 3.5: 40.0 (#126)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Expert | 1441 | 1432 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 33.6% | — |
Multimodal Not comparable
DeepSeek V4 Flash: —, Mistral Medium 3.5: 38.3 (#65)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Vision | — | 1223 |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.0 (#72), Mistral Medium 3.5: 51.9 (#100)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Non-English | 1420 | 1404 |
| LMArena Chinese | 1468 | 1442 |
| LMArena French | 1439 | 1448 |
| LMArena German | 1418 | 1451 |
| LMArena Korean | 1384 | 1385 |
| LMArena Russian | 1428 | 1395 |
| LMArena Spanish | 1436 | 1409 |
| LMArena Japanese | 1406 | — |
Instruction Following Too close to call
DeepSeek V4 Flash: 74.9 (#81), Mistral Medium 3.5: 74.6 (#90)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1415 |
Long Context Too close to call
DeepSeek V4 Flash: 43.8 (#85), Mistral Medium 3.5: 43.2 (#103)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Longer Query | 1434 | 1415 |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 63.8 (#61), Mistral Medium 3.5: 58.5 (#117)
| Benchmark | DeepSeek V4 Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Text | 1432 | 1421 |
| LMArena Creative Writing | 1403 | 1374 |
| LMArena Multi-Turn | 1449 | 1423 |
| EQ-Bench Creative Writing | 1559 | — |
| EQ-Bench 4 | — | 993 |
Frequently asked questions
Is DeepSeek V4 Flash better than Mistral Medium 3.5?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 40.2 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or Mistral Medium 3.5?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Mistral Medium 3.5 lists at $1.50 and $7.50.
Is DeepSeek V4 Flash or Mistral Medium 3.5 better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 36.0 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 262K.
How many benchmarks do DeepSeek V4 Flash and Mistral Medium 3.5 share?
20 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Mistral Medium 3.5 has 22.