Model comparison
DeepSeek V4 Flash vs GPT-5.2
DeepSeek V4 Flash and GPT-5.2 score almost the same on the Noometry Index (53.6 vs 54.1), so choose on price, context window or the category you care about most.
Last verified . 38 shared benchmarks.
DeepSeekSummary
- They share 38 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 3 categories and GPT-5.2 in 5 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.2 leads 59.3 to 55.4.
- The biggest single-benchmark swing is ProofBench: 56% for DeepSeek V4 Flash and 15% for GPT-5.2.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- 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-5.2 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 54.1 |
| Released | 2026-04-24 | 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 | 41 | 67 |
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Category by category
Coding GPT-5.2 leads
DeepSeek V4 Flash: 47.9 (#59), GPT-5.2: 51.6 (#37)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena WebDev | 1582 | 1416 |
| WeirdML | 63% | 72.2% |
| LMArena Coding | 1457 | 1447 |
| ALE-Bench | 1,306 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 49.9% | — |
| GSO | — | 27.4% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GPT-5.2: 40.2 (#24)
| Benchmark | DeepSeek V4 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 DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), GPT-5.2: 50.2 (#35)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| ARC-AGI-2 | 61.4% | 52.9% |
| SimpleBench | 61.1% | 45.8% |
| Kagi LLM Benchmark | 52.2% | 73.3% |
| NYT Connections (extended) | 89.6% | 83.6% |
| ARC-AGI-1 | 89% | 86.2% |
| Chess Puzzles | 33% | 49% |
| LMArena Hard Prompts | 1444 | 1445 |
| Mystery Game Puzzles | 34% | 23% |
| DTBench | 90.9% | 90.9% |
| LMCA | 41.7% | 43.9% |
| Epoch Capabilities Index | 154.49 | 153.45 |
| CritPt | 16.6% | — |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| ForecastBench | — | 60.1 |
Math Too close to call
DeepSeek V4 Flash: 60.3 (#37), GPT-5.2: 60.0 (#38)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 67.4% |
| FrontierMath Tier 4 | 24.4% | 31.7% |
| MathArena Final-Answer Competitions | 76.5% | 72% |
| OTIS Mock AIME 2024-2025 | 94.4% | 96.1% |
| ProofBench | 56% | 15% |
| LMArena Math | 1427 | 1440 |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge GPT-5.2 leads
DeepSeek V4 Flash: 55.4 (#48), GPT-5.2: 59.3 (#32)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 91% | 91.4% |
| SimpleQA Verified | 33.6% | 37.1% |
| LMArena Expert | 1441 | 1445 |
| Humanity's Last Exam | — | 27.8% |
| Vectara Hallucination Rate | — | 8.4% |
Multimodal Not comparable
DeepSeek V4 Flash: —, GPT-5.2: 51.3 (#7)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual Too close to call
DeepSeek V4 Flash: 53.0 (#72), GPT-5.2: 53.4 (#67)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1420 | 1425 |
| LMArena Chinese | 1468 | 1460 |
| LMArena French | 1439 | 1455 |
| LMArena German | 1418 | 1448 |
| LMArena Japanese | 1406 | 1420 |
| LMArena Korean | 1384 | 1392 |
| LMArena Russian | 1428 | 1440 |
| LMArena Spanish | 1436 | 1433 |
Instruction Following Too close to call
DeepSeek V4 Flash: 74.9 (#81), GPT-5.2: 74.7 (#89)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1417 |
Long Context Too close to call
DeepSeek V4 Flash: 43.8 (#85), GPT-5.2: 44.0 (#78)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1434 | 1428 |
| CL-bench | — | 18.2% |
Writing & Preference GPT-5.2 leads
DeepSeek V4 Flash: 63.8 (#61), GPT-5.2: 66.8 (#32)
| Benchmark | DeepSeek V4 Flash | GPT-5.2 |
|---|---|---|
| LMArena Text | 1432 | 1439 |
| LMArena Creative Writing | 1403 | 1401 |
| EQ-Bench Creative Writing | 1559 | 1703 |
| LMArena Multi-Turn | 1449 | 1458 |
Frequently asked questions
Is DeepSeek V4 Flash better than GPT-5.2?
DeepSeek V4 Flash and GPT-5.2 score almost the same on the Noometry Index (53.6 vs 54.1), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek V4 Flash or GPT-5.2?
DeepSeek V4 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 Flash or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 47.9 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-5.2 share?
38 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5.2 has 67.