Model comparison
DeepSeek V4.1 Flash vs GPT-5 Nano
DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 33.4 on the Noometry Index. GPT-5 Nano costs 1.9× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
DeepSeekSummary
- They share 19 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 8 categories and GPT-5 Nano in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4.1 Flash leads 46.2 to 16.4.
- The biggest single-benchmark swing is ProofBench: 54% for DeepSeek V4.1 Flash and 12% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 400K.
- DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4.1 Flash | GPT-5 Nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 50.3 | 33.4 |
| Released | 2026-09-09 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.15 | $0.05 |
| Output $ / M tokens | $0.60 | $0.40 |
| Results tracked | 25 | 48 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.8 (#29), GPT-5 Nano: 33.3 (#248)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1511 | 1351 |
| ALE-Bench | 1,092 | 718.67 |
| SWE-bench Verified (bash only) | — | 34.8% |
| LMArena WebDev | 1620 | — |
| SciCode | 51.9% | — |
| WeirdML | — | 38.1% |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: —, GPT-5 Nano: 25.6 (#99)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 46.2 (#39), GPT-5 Nano: 16.4 (#291)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Hard Prompts | 1485 | 1329 |
| DTBench | 89.9% | 62.7% |
| LMCA | 47% | 7.9% |
| Epoch Capabilities Index | 155.01 | 139.39 |
| ARC-AGI-2 | — | 2.6% |
| Kagi LLM Benchmark | — | 62.2% |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | — | 20.7% |
| CritPt | 14.3% | — |
| Chess Puzzles | — | 27% |
| Mystery Game Puzzles | — | 9% |
| Surface Evolver Bench | 46.3% | — |
| ForecastBench | — | 59.1 |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 48.4 (#52), GPT-5 Nano: 29.1 (#221)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| ProofBench | 54% | 12% |
| LMArena Math | 1493 | 1318 |
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| OTIS Mock AIME 2024-2025 | — | 81.1% |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 41.6 (#97), GPT-5 Nano: 35.8 (#166)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Expert | 1505 | 1321 |
| GPQA Diamond | — | 69.4% |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
Multimodal Not comparable
DeepSeek V4.1 Flash: —, GPT-5 Nano: 30.7 (#102)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.5 (#29), GPT-5 Nano: 45.5 (#167)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1452 | 1313 |
| LMArena Chinese | 1506 | 1355 |
| LMArena Russian | 1473 | 1296 |
| LMArena Spanish | 1499 | 1360 |
| LMArena French | 1448 | — |
| LMArena German | — | 1329 |
| LMArena Korean | — | 1269 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.6 (#24), GPT-5 Nano: 75.2 (#72)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1475 | 1306 |
| IFEval | — | 93.2% |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#46), GPT-5 Nano: 31.3 (#275)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1476 | 1313 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 65.8 (#43), GPT-5 Nano: 39.4 (#244)
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1465 | 1320 |
| LMArena Creative Writing | 1436 | 1250 |
| EQ-Bench Creative Writing | 1540 | 705 |
| LMArena Multi-Turn | 1460 | 1311 |
| WildBench | — | 80.6% |
Frequently asked questions
Is DeepSeek V4.1 Flash better than GPT-5 Nano?
DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 33.4 on the Noometry Index. GPT-5 Nano costs 1.9× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4.1 Flash or GPT-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; DeepSeek V4.1 Flash lists at $0.15 and $0.60.
Is DeepSeek V4.1 Flash or GPT-5 Nano better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.8 versus 33.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4.1 Flash does, with 1M tokens against 400K.
How many benchmarks do DeepSeek V4.1 Flash and GPT-5 Nano share?
19 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and GPT-5 Nano has 48.