Model comparison
DeepSeek V4.1 Flash vs GPT-5.2
GPT-5.2 is the stronger model overall, scoring 54.1 to 50.3 on the Noometry Index. DeepSeek V4.1 Flash costs 18× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
DeepSeekSummary
- They share 22 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 4 categories and GPT-5.2 in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.2 leads 59.2 to 41.6.
- The biggest single-benchmark swing is ProofBench: 54% for DeepSeek V4.1 Flash and 15% for GPT-5.2.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- 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-5.2 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 50.3 | 54.1 |
| Released | 2026-09-09 | 2025-12-11 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.15 | $1.75 |
| Output $ / M tokens | $0.60 | $14 |
| Results tracked | 25 | 67 |
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Category by category
Coding Too close to call
DeepSeek V4.1 Flash: 52.8 (#29), GPT-5.2: 52.2 (#32)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena WebDev | 1620 | 1415 |
| LMArena Coding | 1511 | 1447 |
| ALE-Bench | 1,092 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 51.9% | — |
| GSO | — | 27.4% |
| WeirdML | — | 72.2% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: —, GPT-5.2: 40.0 (#24)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| Terminal-Bench | — | 64.9% |
| Berkeley Function Calling Leaderboard | — | 55.9% |
| GDPval | — | 49.7% |
| Remote Labor Index | — | 2.5% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| DeepResearch Bench | — | 41.1% |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |
| Vending-Bench 2 | — | 3,591 |
Reasoning GPT-5.2 leads
DeepSeek V4.1 Flash: 46.2 (#39), GPT-5.2: 50.1 (#33)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| NYT Connections (extended) | 89.6% | 83.6% |
| LMArena Hard Prompts | 1485 | 1445 |
| DTBench | 89.9% | 90.9% |
| LMCA | 47% | 43.9% |
| Epoch Capabilities Index | 155.01 | 153.45 |
| ARC-AGI-2 | — | 52.9% |
| SimpleBench | — | 45.8% |
| Kagi LLM Benchmark | — | 73.3% |
| ARC-AGI-1 | — | 86.2% |
| CritPt | 14.3% | — |
| Chess Puzzles | — | 49% |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Mystery Game Puzzles | — | 23% |
| Surface Evolver Bench | 46.3% | — |
| ForecastBench | — | 60.1 |
Math GPT-5.2 leads
DeepSeek V4.1 Flash: 48.4 (#52), GPT-5.2: 59.8 (#32)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| ProofBench | 54% | 15% |
| LMArena Math | 1493 | 1439 |
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
| OTIS Mock AIME 2024-2025 | — | 96.1% |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge GPT-5.2 leads
DeepSeek V4.1 Flash: 41.6 (#97), GPT-5.2: 59.2 (#29)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Expert | 1505 | 1445 |
| GPQA Diamond | — | 91.4% |
| Humanity's Last Exam | — | 27.8% |
| SimpleQA Verified | — | 37.1% |
| Vectara Hallucination Rate | — | 8.4% |
Multimodal Not comparable
DeepSeek V4.1 Flash: —, GPT-5.2: 50.9 (#7)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.5 (#29), GPT-5.2: 53.6 (#63)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1452 | 1425 |
| LMArena Chinese | 1506 | 1460 |
| LMArena French | 1448 | 1456 |
| LMArena Russian | 1473 | 1440 |
| LMArena Spanish | 1499 | 1433 |
| LMArena German | — | 1448 |
| LMArena Japanese | — | 1421 |
| LMArena Korean | — | 1390 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.6 (#24), GPT-5.2: 74.9 (#83)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1475 | 1417 |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#46), GPT-5.2: 44.0 (#74)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1476 | 1428 |
| CL-bench | — | 18.2% |
Writing & Preference GPT-5.2 leads
DeepSeek V4.1 Flash: 65.8 (#43), GPT-5.2: 67.0 (#30)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 |
|---|---|---|
| LMArena Text | 1465 | 1439 |
| LMArena Creative Writing | 1436 | 1402 |
| EQ-Bench Creative Writing | 1540 | 1703 |
| LMArena Multi-Turn | 1460 | 1458 |
Frequently asked questions
Is DeepSeek V4.1 Flash better than GPT-5.2?
GPT-5.2 is the stronger model overall, scoring 54.1 to 50.3 on the Noometry Index. DeepSeek V4.1 Flash costs 18× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4.1 Flash or GPT-5.2?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is DeepSeek V4.1 Flash or GPT-5.2 better for coding?
They score almost the same on coding (52.8 vs 52.2); test both on your own repository before choosing.
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-5.2 share?
22 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and GPT-5.2 has 67.