Model comparison
DeepSeek-V3.2-Exp vs GPT-5.5 Pro
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 233× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
DeepSeekSummary
- They share 11 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 0 categories and GPT-5.5 Pro in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 Pro leads 73.3 to 22.1.
- The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 84.6% for GPT-5.5 Pro.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $30 / $180 for GPT-5.5 Pro.
- GPT-5.5 Pro 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-5.5 Pro | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 64.3 |
| Released | 2025-09-29 | 2026-04-23 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $0.26 | $30 |
| Output $ / M tokens | $0.38 | $180 |
| Results tracked | 49 | 14 |
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Category by category
Coding Not comparable
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning GPT-5.5 Pro leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-5.5 Pro: 73.3 (#10)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| ARC-AGI-2 | 4% | 84.6% |
| ARC-AGI-1 | 57% | 96.5% |
| CritPt | 2.9% | 30.6% |
| Chess Puzzles | 14% | 64% |
| DTBench | 87.7% | 96% |
| LMCA | 29.1% | 53.9% |
| Epoch Capabilities Index | 146.27 | 162.07 |
| SimpleBench | — | 76.9% |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
Math GPT-5.5 Pro leads
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-5.5 Pro: 84.0 (#10)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 100% |
| FrontierMath (Feb 2025 set) | 22.1% | 52.4% |
| FrontierMath Tier 4 (v1) | 2.1% | 39.6% |
| FrontierMath (Tiers 1-3) | — | 87.7% |
| FrontierMath Tier 4 | — | 78% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
Knowledge GPT-5.5 Pro leads
DeepSeek-V3.2-Exp: 51.7 (#66), GPT-5.5 Pro: 64.1 (#19)
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| GPQA Diamond | 83.4% | 93.9% |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |
Multilingual Not comparable
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |
Instruction Following Not comparable
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| LMArena Instruction Following | 1413 | — |
Long Context Not comparable
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-5.5 Pro: —
| Benchmark | DeepSeek-V3.2-Exp | GPT-5.5 Pro |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-5.5 Pro?
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 233× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.5 Pro?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.5 Pro lists at $30 and $180.
Which has the bigger context window?
GPT-5.5 Pro does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.5 Pro share?
11 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-5.5 Pro has 14.