Model comparison
DeepSeek V4 Flash vs GPT-5 Mini
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 41.7 on the Noometry Index.
Last verified . 36 shared benchmarks.
DeepSeekSummary
- They share 36 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 7 categories and GPT-5 Mini in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 24.1.
- The biggest single-benchmark swing is ARC-AGI-2: 61.4% for DeepSeek V4 Flash and 4.4% for GPT-5 Mini.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 400K.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Flash | GPT-5 Mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 41.7 |
| Released | 2026-04-24 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.15 | $0.25 |
| Output $ / M tokens | $0.60 | $2 |
| Results tracked | 41 | 60 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.6 (#56), GPT-5 Mini: 40.0 (#134)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| SciCode | 49.9% | 39.2% |
| WeirdML | 63% | 52.7% |
| LMArena Coding | 1457 | 1406 |
| ALE-Bench | 1,306 | 799.77 |
| SWE-bench Verified | — | 64.7% |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 59.8% |
| LMArena WebDev | 1581 | — |
| SWE-bench Multilingual | — | 39.7% |
| AlgoTune | — | 1.38 |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GPT-5 Mini: 31.0 (#64)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| Terminal-Bench | — | 34.8% |
| Berkeley Function Calling Leaderboard | — | 55.5% |
| Vending-Bench 2 | — | -31.18 |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#25), GPT-5 Mini: 24.1 (#153)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| ARC-AGI-2 | 61.4% | 4.4% |
| Kagi LLM Benchmark | 52.2% | 70.3% |
| ARC-AGI-1 | 89% | 54.3% |
| CritPt | 16.6% | 0% |
| Chess Puzzles | 33% | 30% |
| LMArena Hard Prompts | 1443 | 1380 |
| Mystery Game Puzzles | 34% | 10% |
| DTBench | 90.9% | 80.5% |
| LMCA | 41.7% | 34.2% |
| Epoch Capabilities Index | 154.51 | 145.51 |
| SimpleBench | 61.1% | — |
| NYT Connections (extended) | 89.6% | — |
| EnigmaEval | — | 8.2% |
| ForecastBench | — | 61 |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.0 (#31), GPT-5 Mini: 46.3 (#59)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 46.7% |
| FrontierMath Tier 4 | 24.4% | 12.2% |
| OTIS Mock AIME 2024-2025 | 94.4% | 86.7% |
| ProofBench | 56% | 9% |
| LMArena Math | 1427 | 1377 |
| MathArena Final-Answer Competitions | 76.5% | — |
| Omni-MATH | — | 72.2% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 27.2% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.5 (#43), GPT-5 Mini: 45.5 (#80)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| GPQA Diamond | 91% | 75% |
| SimpleQA Verified | 33.6% | 21.6% |
| LMArena Expert | 1441 | 1377 |
| Humanity's Last Exam | — | 19.4% |
| MMLU-Pro | — | 83.5% |
| Confabulations | — | 13.3% |
| Vectara Hallucination Rate | — | 12.9% |
| GPQA (HELM) | — | 75.6% |
Multimodal Not comparable
DeepSeek V4 Flash: —, GPT-5 Mini: 35.0 (#80)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| LMArena Vision | — | 1202 |
| VPCT | — | 40.2% |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.2 (#71), GPT-5 Mini: 49.1 (#131)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| LMArena Non-English | 1420 | 1363 |
| LMArena Chinese | 1467 | 1387 |
| LMArena French | 1438 | 1385 |
| LMArena German | 1418 | 1366 |
| LMArena Japanese | 1403 | 1342 |
| LMArena Korean | 1383 | 1306 |
| LMArena Russian | 1428 | 1362 |
| LMArena Spanish | 1437 | 1355 |
Instruction Following GPT-5 Mini leads
DeepSeek V4 Flash: 75.1 (#76), GPT-5 Mini: 76.4 (#43)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| LMArena Instruction Following | 1421 | 1357 |
| IFEval | — | 92.7% |
Long Context DeepSeek V4 Flash leads
DeepSeek V4 Flash: 43.8 (#81), GPT-5 Mini: 41.9 (#127)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| LMArena Longer Query | 1435 | 1355 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 64.0 (#59), GPT-5 Mini: 55.4 (#143)
| Benchmark | DeepSeek V4 Flash | GPT-5 Mini |
|---|---|---|
| LMArena Text | 1432 | 1373 |
| LMArena Creative Writing | 1403 | 1325 |
| EQ-Bench Creative Writing | 1559 | 1313 |
| LMArena Multi-Turn | 1449 | 1363 |
| Short-Story Creative Writing | — | 83.1% |
| WildBench | — | 85.5% |
Frequently asked questions
Is DeepSeek V4 Flash better than GPT-5 Mini?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 41.7 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or GPT-5 Mini?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5 Mini lists at $0.25 and $2.
Is DeepSeek V4 Flash or GPT-5 Mini better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.6 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 400K.
How many benchmarks do DeepSeek V4 Flash and GPT-5 Mini share?
36 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5 Mini has 60.