Coding
Directional only- DeepSeek V3.2 (Thinking)
- 49.6
- Estimated · #76/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 1 vs 9 public rows
- Reading
- Directional only
DeepSeek V3.2 (Thinking) vs GPT-5.5
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 73.27 versus 57.54, and the 90% score intervals do not overlap.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V3.2 (Thinking)
DeepSeek V3.2 (Thinking) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V3.2 (Thinking)
DeepSeek V3.2 (Thinking) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V3.2 (Thinking) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V3.2 (Thinking) is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3.2 (Thinking) does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Shared
37 GPT-5.5 only
- Shared results
- 1
- DeepSeek V3.2 (Thinking) only
- 0
- GPT-5.5 only
- 37
- Like-for-like categories
- 0 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 0 vs 13 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3.2 (Thinking)
- Not ranked
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3.2 (Thinking) | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 49.6Estimated · #76/183 | 67.7Supported · #8/183 | Directional onlyBenchAlign lane · 1 vs 9 public rows | Directional only |
| Agentic | Not ranked | 63.9Supported · #15/151 | Not comparableBenchAlign lane · 0 vs 13 public rows | Not comparable |
| Reasoning | Not ranked | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 73.3Supported · #7/181 | Not comparableBenchAlign lane · 0 vs 6 public rows | Not comparable |
| Math | Not ranked | 69.6Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 71.3#19/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.9#7/120 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- DeepSeek V3.2 (Thinking)
- $0.00165
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
DeepSeek V3.2 (Thinking) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3.2 (Thinking)
- $0.03407
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
DeepSeek V3.2 (Thinking) has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V3.2 (Thinking)
- $0.0609
- Does not fit in one request
- GPT-5.5
- $0.5
- Fits in one request
DeepSeek V3.2 (Thinking) does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V3.2 (Thinking)GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3.2 (Thinking)
128K
GPT-5.5
API model ID
DeepSeek V3.2 (Thinking)
Not sourced
GPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V3.2 (Thinking)
$0.14 per 1M cached input tokens
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
DeepSeek V3.2 (Thinking)
Not sourced
GPT-5.5
Not sourced
Documented outputs
DeepSeek V3.2 (Thinking)
Not sourced
GPT-5.5
Not sourced
Provider availability
DeepSeek V3.2 (Thinking)
Not sourced
GPT-5.5
Not sourced
Reasoning profile
DeepSeek V3.2 (Thinking)
Reasoning
GPT-5.5
Reasoning
Weight access
DeepSeek V3.2 (Thinking)
Open Weight
GPT-5.5
Proprietary
License
DeepSeek V3.2 (Thinking)
Open Weight
GPT-5.5
Proprietary
Release date
DeepSeek V3.2 (Thinking)
2025-12-01
GPT-5.5
2026-04-23
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.5 has the higher public score, 73.27 versus 57.54, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.03407 vs $0.34. Cache-heavy agent loop: $0.0609 vs $0.5.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence38 rows
Agentic
Terminal-Bench 2.0
DeepSeek V3.2 (Thinking)—GPT-5.582%SourceNot directly comparable
CyberGym
DeepSeek V3.2 (Thinking)—GPT-5.581.8%SourceNot directly comparable
BrowseComp
DeepSeek V3.2 (Thinking)—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
DeepSeek V3.2 (Thinking)—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
DeepSeek V3.2 (Thinking)—GPT-5.575.3%SourceNot directly comparable
Toolathlon
DeepSeek V3.2 (Thinking)—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
DeepSeek V3.2 (Thinking)—GPT-5.598%SourceNot directly comparable
Gert Labs
DeepSeek V3.2 (Thinking)—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
DeepSeek V3.2 (Thinking)—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
DeepSeek V3.2 (Thinking)—GPT-5.513.0%SourceNot directly comparable
JobBench
DeepSeek V3.2 (Thinking)—GPT-5.542.7%SourceNot directly comparable
ExploitGym
DeepSeek V3.2 (Thinking)—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V3.2 (Thinking)—GPT-5.576.4%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceDeepSeek V3.2 (Thinking)5.11%GPT-5.569.85%GPT-5.5 leads this result
SWE-bench Pro
DeepSeek V3.2 (Thinking)—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
DeepSeek V3.2 (Thinking)—GPT-5.582.0%SourceNot directly comparable
React Native Evals
DeepSeek V3.2 (Thinking)—GPT-5.584.7%SourceNot directly comparable
cursorBench31
DeepSeek V3.2 (Thinking)—GPT-5.559.2%SourceNot directly comparable
cursorBench32
DeepSeek V3.2 (Thinking)—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
DeepSeek V3.2 (Thinking)—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
DeepSeek V3.2 (Thinking)—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
DeepSeek V3.2 (Thinking)—GPT-5.582.6%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
DeepSeek V3.2 (Thinking)—GPT-5.583.1%SourceNot directly comparable
MRCR v2 128K-256K
DeepSeek V3.2 (Thinking)—GPT-5.587.5%SourceNot directly comparable
ARC-AGI-2
DeepSeek V3.2 (Thinking)—GPT-5.585%SourceNot directly comparable
ARC-AGI-3
DeepSeek V3.2 (Thinking)—GPT-5.50.4%SourceNot directly comparable
Knowledge
GPQA
DeepSeek V3.2 (Thinking)—GPT-5.593.6%SourceNot directly comparable
GPQA-D
DeepSeek V3.2 (Thinking)—GPT-5.593.6%SourceNot directly comparable
HLE
DeepSeek V3.2 (Thinking)—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
DeepSeek V3.2 (Thinking)—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
DeepSeek V3.2 (Thinking)—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
DeepSeek V3.2 (Thinking)—GPT-5.588.1%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, DeepSeek V3.2 (Thinking) or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 57.54, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, DeepSeek V3.2 (Thinking) or GPT-5.5?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 49.6. DeepSeek V3.2 (Thinking) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, DeepSeek V3.2 (Thinking) or GPT-5.5?
DeepSeek V3.2 (Thinking) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V3.2 (Thinking) or GPT-5.5?
For the stated presets, chat costs $0.00164 on DeepSeek V3.2 (Thinking) and $0.02 on GPT-5.5; repository review costs $0.03407 and $0.34; the cache-heavy agent loop costs $0.0609 and $0.5. DeepSeek V3.2 (Thinking) does not fit this workload in one request.
Which has the larger context window, DeepSeek V3.2 (Thinking) or GPT-5.5?
GPT-5.5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 4, 2026
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