Model comparison
DeepSeek V4 Flash vs Mixtral 8x22B
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.1 on the Noometry Index.
Last verified . 21 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 21 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Mixtral 8x22B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek V4 Flash leads 55.4 to 15.1.
- The biggest single-benchmark swing is WeirdML: 63% for DeepSeek V4 Flash and 3.2% for Mixtral 8x22B.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 64K.
Side by side
| DeepSeek V4 Flash | Mixtral 8x22B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 53.6 | 27.1 |
| Released | 2026-04-24 | 2024-04-17 |
| Weights | Open | Open |
| Context window | 1M | 64K |
| Max output | 393K | 64K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.60 | $6 |
| Results tracked | 41 | 34 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.9 (#59), Mixtral 8x22B: 24.2 (#329)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| WeirdML | 63% | 3.2% |
| LMArena Coding | 1457 | 1166 |
| FrontierCode | 18.8% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 49.9% | — |
| BigCodeBench Instruct | — | 40.6% |
| BigCodeBench Complete | — | 50.2% |
| ALE-Bench | 1,306 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, Mixtral 8x22B: 23.1 (#127)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| Cybench | — | 7.5% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), Mixtral 8x22B: 19.9 (#248)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Hard Prompts | 1444 | 1150 |
| DTBench | 90.9% | 55.1% |
| Epoch Capabilities Index | 154.49 | 122.03 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| CritPt | 16.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
| LMCA | 41.7% | — |
| ForecastBench | — | 56.3 |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), Mixtral 8x22B: 22.9 (#275)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Math | 1427 | 1184 |
| 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% | — |
| Omni-MATH | — | 16.3% |
| MATH Level 5 | — | 24.2% |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), Mixtral 8x22B: 15.1 (#293)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | 91% | 34.1% |
| LMArena Expert | 1441 | 1113 |
| SimpleQA Verified | 33.6% | — |
| MMLU-Pro | — | 46% |
| GPQA (HELM) | — | 33.4% |
| MMLU | — | 77.8% |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.0 (#72), Mixtral 8x22B: 32.8 (#255)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | 1420 | 1128 |
| LMArena Chinese | 1468 | 1116 |
| LMArena French | 1439 | 1166 |
| LMArena German | 1418 | 1141 |
| LMArena Japanese | 1406 | 1037 |
| LMArena Korean | 1384 | 1057 |
| LMArena Russian | 1428 | 1158 |
| LMArena Spanish | 1436 | 1151 |
Instruction Following DeepSeek V4 Flash leads
DeepSeek V4 Flash: 74.9 (#81), Mixtral 8x22B: 57.7 (#266)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Instruction Following | 1421 | 1147 |
| IFEval | — | 72.4% |
Long Context DeepSeek V4 Flash leads
DeepSeek V4 Flash: 43.8 (#85), Mixtral 8x22B: 34.7 (#247)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | 1434 | 1144 |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 63.8 (#61), Mixtral 8x22B: 36.9 (#262)
| Benchmark | DeepSeek V4 Flash | Mixtral 8x22B |
|---|---|---|
| LMArena Text | 1432 | 1162 |
| LMArena Creative Writing | 1403 | 1141 |
| LMArena Multi-Turn | 1449 | 1130 |
| EQ-Bench Creative Writing | 1559 | — |
| WildBench | — | 71.1% |
Frequently asked questions
Is DeepSeek V4 Flash better than Mixtral 8x22B?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.1 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or Mixtral 8x22B?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Mixtral 8x22B lists at $2 and $6.
Is DeepSeek V4 Flash or Mixtral 8x22B better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 24.2 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 64K.
How many benchmarks do DeepSeek V4 Flash and Mixtral 8x22B share?
21 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Mixtral 8x22B has 34.