Model comparison
DeepSeek-V3.2-Exp vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 28× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
DeepSeekSummary
- They share 35 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and GPT-5.6 Sol in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.7 to 22.0.
- The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 92.5% for GPT-5.6 Sol.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | GPT-5.6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.9 | 64.4 |
| Released | 2025-09-29 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.26 | $4 |
| Output $ / M tokens | $0.38 | $20 |
| Results tracked | 48 | 64 |
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Category by category
Coding GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 46.4 (#61), GPT-5.6 Sol: 61.1 (#7)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena WebDev | 1362 | 1619 |
| SciCode | 38.9% | 57.1% |
| WeirdML | 39.5% | 89.4% |
| LMArena Coding | 1454 | 1493 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| CursorBench | — | 41.7% |
| SWE-bench Multilingual | 59% | — |
| FrontierSWE | — | 32.2% |
| MirrorCode | — | 20% |
| ALE-Bench | — | 2,177 |
Agentic & Tool Use GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 37.9 (#31), GPT-5.6 Sol: 50.2 (#7)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| Vending-Bench 2 | 1,034 | 9,619 |
| Terminal-Bench | 39.6% | — |
| APEX-Agents | — | 51.4% |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| OSWorld 2.0 | — | 27.3% |
| TheAgentCompany | 42.9% | — |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
Reasoning GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 22.0 (#185), GPT-5.6 Sol: 74.7 (#6)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| ARC-AGI-2 | 4% | 92.5% |
| Kagi LLM Benchmark | 52.2% | 67% |
| NYT Connections (extended) | 36.7% | 93.8% |
| ARC-AGI-1 | 57% | 97.5% |
| CritPt | 2.9% | 32.3% |
| Chess Puzzles | 14% | 64% |
| LMArena Hard Prompts | 1433 | 1481 |
| DTBench | 87.7% | 96% |
| LMCA | 29.1% | 59.2% |
| Epoch Capabilities Index | 146.28 | 161.99 |
| SimpleBench | — | 71.7% |
| EnigmaEval | — | 37.1% |
| Thematic Generalization | 65% | — |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
Math GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 40.8 (#75), GPT-5.6 Sol: 85.5 (#7)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 100% |
| ProofBench | 8% | 83% |
| LMArena Math | 1435 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| MathArena Final-Answer Competitions | 57.7% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 51.6 (#61), GPT-5.6 Sol: 64.3 (#16)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 83.4% | 93.5% |
| Vectara Hallucination Rate | 5.3% | 12.4% |
| LMArena Expert | 1436 | 1517 |
| SimpleQA Verified | — | 69.7% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-5.6 Sol: 48.9 (#8)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 52.4 (#85), GPT-5.6 Sol: 55.4 (#32)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1409 | 1450 |
| LMArena Chinese | 1461 | 1523 |
| LMArena French | 1435 | 1475 |
| LMArena German | 1440 | 1476 |
| LMArena Japanese | 1377 | 1475 |
| LMArena Korean | 1372 | 1434 |
| LMArena Russian | 1423 | 1466 |
| LMArena Spanish | 1441 | 1449 |
Instruction Following GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 74.7 (#89), GPT-5.6 Sol: 77.8 (#16)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1413 | 1480 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), GPT-5.6 Sol: 45.3 (#42)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1428 | 1479 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference GPT-5.6 Sol leads
DeepSeek-V3.2-Exp: 62.5 (#73), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1425 | 1456 |
| LMArena Creative Writing | 1403 | 1450 |
| EQ-Bench Creative Writing | 1515 | 1972 |
| LMArena Multi-Turn | 1427 | 1456 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 28× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.6 Sol?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is DeepSeek-V3.2-Exp or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 46.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.6 Sol share?
35 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-5.6 Sol has 64.