Agentic
Directional only- DeepSeek V4 Flash 0731
- 63.8
- GPT-5.5
- 81.6
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
DeepSeek V4 Flash 0731 vs GPT-5.5
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
10 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 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 V4 Flash 0731
DeepSeek V4 Flash 0731 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
23 DeepSeek V4 Flash 0731 only
10 Shared
28 GPT-5.5 only
- Shared results
- 10
- DeepSeek V4 Flash 0731 only
- 23
- GPT-5.5 only
- 28
- Like-for-like categories
- 0 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- DeepSeek V4 Flash 0731
- 68.8
- GPT-5.5
- 58.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Knowledge
Directional only- DeepSeek V4 Flash 0731
- 55.3
- GPT-5.5
- 57.8
- Weighted basis
- 4 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4 Flash 0731
- Not measured
- GPT-5.5
- 85.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Flash 0731
- 94.8
- GPT-5.5
- 47.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Flash 0731
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Flash 0731
- Not measured
- GPT-5.5
- 70.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Flash 0731
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek V4 Flash 0731 | GPT-5.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 63.8 | 81.6 | Directional only2 vs 3 rows | Directional only |
| Coding | 68.8 | 58.6 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 55.3 | 57.8 | Directional only4 vs 2 rows | Directional only |
| Reasoning | Not measured | 85.0 | Not comparable0 vs 1 rows | Not comparable |
| Math | 94.8 | 47.6 | Not comparable1 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 70.4 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
Terminal-Bench 2.0
Agentic
DeepSeek V4 Flash 0731: 56.9%GPT-5.5: 82%Normalized gap 25.1DeepSeek V4 Flash 0731 source GPT-5.5 sourceHLE
Knowledge
DeepSeek V4 Flash 0731: 34.8%GPT-5.5: 52.2%Normalized gap 17.4DeepSeek V4 Flash 0731 source GPT-5.5 sourceBrowseComp
Agentic
DeepSeek V4 Flash 0731: 73.2%GPT-5.5: 84.4%Normalized gap 11.2DeepSeek V4 Flash 0731 source GPT-5.5 sourceSWE-bench Pro
Coding
DeepSeek V4 Flash 0731: 52.6%GPT-5.5: 58.6%Normalized gap 6.0DeepSeek V4 Flash 0731 source GPT-5.5 sourceGPQA
Knowledge
DeepSeek V4 Flash 0731: 88.1%GPT-5.5: 93.6%Normalized gap 5.5DeepSeek V4 Flash 0731 source GPT-5.5 source
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 V4 Flash 0731
- $0.00028
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
DeepSeek V4 Flash 0731 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Flash 0731
- $0.00784
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
DeepSeek V4 Flash 0731 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 V4 Flash 0731
- $0.00616
- Fits in one request
- GPT-5.5
- $0.5
- Fits in one request
DeepSeek V4 Flash 0731 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4 Flash 0731GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Flash 0731
GPT-5.5
API model ID
DeepSeek V4 Flash 0731
deepseek-v4-flash
DeepSeek V4 Flash 0731 updateGPT-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 V4 Flash 0731
$0.0028 per 1M cached input tokens
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
DeepSeek V4 Flash 0731
GPT-5.5
Not sourced
Documented outputs
DeepSeek V4 Flash 0731
GPT-5.5
Not sourced
Provider availability
DeepSeek V4 Flash 0731
Public Beta · DeepSeek API
DeepSeek V4 Flash 0731 updateGPT-5.5
Not sourced
Reasoning profile
DeepSeek V4 Flash 0731
Reasoning
GPT-5.5
Reasoning
Weight access
DeepSeek V4 Flash 0731
Proprietary
GPT-5.5
Proprietary
License
DeepSeek V4 Flash 0731
Proprietary
GPT-5.5
Proprietary
Release date
DeepSeek V4 Flash 0731
2026-07-31
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.00784 vs $0.34. Cache-heavy agent loop: $0.00616 vs $0.5.
- Context tradeoff
- Both models list 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 evidence61 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Flash 073156.9%SourceGPT-5.582%SourceGPT-5.5 leads this result
BrowseComp
DeepSeek V4 Flash 073173.2%SourceGPT-5.584.4%SourceGPT-5.5 leads this result
HLE w/ tools
DeepSeek V4 Flash 073145.1%SourceGPT-5.5—Not directly comparable
MCP Atlas
DeepSeek V4 Flash 073169%SourceGPT-5.575.3%SourceGPT-5.5 leads this result
Toolathlon
DeepSeek V4 Flash 073147.8%SourceGPT-5.555.6%SourceGPT-5.5 leads this result
Terminal-Bench 2.1
DeepSeek V4 Flash 073182.7%SourceGPT-5.5—Not directly comparable
CyberGym
DeepSeek V4 Flash 073176.7%SourceGPT-5.581.8%SourceGPT-5.5 leads this result
Toolathlon-Verified
DeepSeek V4 Flash 073170.3%SourceGPT-5.5—Not directly comparable
Agents' Last Exam
DeepSeek V4 Flash 073125.2%SourceGPT-5.5—Not directly comparable
AutomationBench
DeepSeek V4 Flash 073125.1%SourceGPT-5.5—Not directly comparable
OSWorld-Verified
DeepSeek V4 Flash 0731—GPT-5.578.7%SourceNot directly comparable
τ²-bench results
DeepSeek V4 Flash 0731—GPT-5.598%SourceNot directly comparable
Gert Labs
DeepSeek V4 Flash 0731—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
DeepSeek V4 Flash 0731—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
DeepSeek V4 Flash 0731—GPT-5.513.0%SourceNot directly comparable
JobBench
DeepSeek V4 Flash 0731—GPT-5.542.7%SourceNot directly comparable
ExploitGym
DeepSeek V4 Flash 0731—GPT-5.513.4%SourceNot directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Flash 073191.6%SourceGPT-5.5—Not directly comparable
Codeforces
DeepSeek V4 Flash 07313052.0SourceGPT-5.5—Not directly comparable
SWE-bench Verified
DeepSeek V4 Flash 073179%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
DeepSeek V4 Flash 073152.6%SourceGPT-5.558.6%SourceGPT-5.5 leads this result
SWE Multilingual
DeepSeek V4 Flash 073173.3%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Flash 073156.9%SourceGPT-5.582.0%SourceGPT-5.5 leads this result
Terminal-Bench 2.1
DeepSeek V4 Flash 073182.7%SourceGPT-5.5—Not directly comparable
NL2Repo
DeepSeek V4 Flash 073154.2%SourceGPT-5.5—Not directly comparable
deepSwe
DeepSeek V4 Flash 073154.4%SourceGPT-5.5—Not directly comparable
DSBench-FullStack
DeepSeek V4 Flash 073168.7%SourceGPT-5.5—Not directly comparable
DSBench-Hard
DeepSeek V4 Flash 073159.6%SourceGPT-5.5—Not directly comparable
Vibe Code Bench
DeepSeek V4 Flash 0731—GPT-5.569.85%SourceNot directly comparable
React Native Evals
DeepSeek V4 Flash 0731—GPT-5.584.7%SourceNot directly comparable
cursorBench31
DeepSeek V4 Flash 0731—GPT-5.559.2%SourceNot directly comparable
cursorBench32
DeepSeek V4 Flash 0731—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
DeepSeek V4 Flash 0731—GPT-5.543.0%SourceNot directly comparable
APEX-SWE
DeepSeek V4 Flash 0731—GPT-5.537.0%SourceNot directly comparable
EEBench
DeepSeek V4 Flash 0731—GPT-5.542.3%SourceNot directly comparable
CADGenBench Generation
DeepSeek V4 Flash 0731—GPT-5.529.7%SourceNot directly comparable
SpaceXAI MTS Eval
DeepSeek V4 Flash 0731—GPT-5.546.4%SourceNot directly comparable
InferenceEval
DeepSeek V4 Flash 0731—GPT-5.538.9%SourceNot directly comparable
Reasoning
MRCR 1M
DeepSeek V4 Flash 073178.7%SourceGPT-5.5—Not directly comparable
CorpusQA 1M
DeepSeek V4 Flash 073160.5%SourceGPT-5.5—Not directly comparable
MRCR v2 64K-128K
DeepSeek V4 Flash 0731—GPT-5.583.1%SourceNot directly comparable
MRCR v2 128K-256K
DeepSeek V4 Flash 0731—GPT-5.587.5%SourceNot directly comparable
ARC-AGI-2
DeepSeek V4 Flash 0731—GPT-5.585%SourceNot directly comparable
ARC-AGI-3
DeepSeek V4 Flash 0731—GPT-5.50.4%SourceNot directly comparable
Knowledge
MMLU-Pro
DeepSeek V4 Flash 073186.2%SourceGPT-5.5—Not directly comparable
SimpleQA
DeepSeek V4 Flash 073134.1%SourceGPT-5.5—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Flash 073178.9%SourceGPT-5.5—Not directly comparable
GPQA
DeepSeek V4 Flash 073188.1%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
GPQA-D
DeepSeek V4 Flash 073188.1%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
HLE
DeepSeek V4 Flash 073134.8%SourceGPT-5.552.2%SourceGPT-5.5 leads this result
HLE w/o tools
DeepSeek V4 Flash 0731—GPT-5.541.4%SourceNot directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Flash 073194.8%SourceGPT-5.5—Not directly comparable
IMOAnswerBench
DeepSeek V4 Flash 073188.4%SourceGPT-5.5—Not directly comparable
Apex
DeepSeek V4 Flash 073133.0%SourceGPT-5.5—Not directly comparable
Apex Shortlist
DeepSeek V4 Flash 073185.7%SourceGPT-5.5—Not directly comparable
FrontierMath (legacy)
DeepSeek V4 Flash 0731—GPT-5.551.7%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
DeepSeek V4 Flash 0731—GPT-5.551.700%SourceNot directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V4 Flash 0731—GPT-5.535.400%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, DeepSeek V4 Flash 0731 or GPT-5.5?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek V4 Flash 0731 or GPT-5.5?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, DeepSeek V4 Flash 0731 or GPT-5.5?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, DeepSeek V4 Flash 0731 or GPT-5.5?
For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash 0731 and $0.02 on GPT-5.5; repository review costs $0.00784 and $0.34; the cache-heavy agent loop costs $0.00616 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, DeepSeek V4 Flash 0731 or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
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