Model A

DeepSeek

43.74/100

Supported · Public rank #175

90% interval 27.7–59.7

DeepSeek V3 vs GPT-5.5

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GPT-5.5

OpenAI

73.27/100

Supported · Public rank #9

90% interval 71.0–75.6

Decision reading

GPT-5.5 has the higher public score, 73.27 versus 43.74, and the 90% score intervals do not overlap.

2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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
DeepSeek V3 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
DeepSeek V3 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 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 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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 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.

4 DeepSeek V3 only

2 Shared

36 GPT-5.5 only

Shared results: 2
DeepSeek V3 only: 4
GPT-5.5 only: 36
Like-for-like categories: 0 / 8

4 categories rest 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

Directional only

DeepSeek V3: 38.5; Estimated · #124/151
GPT-5.5: 63.9; Supported · #15/151
Basis: BenchAlign lane · 0 vs 13 public rows
Reading: Directional only

Coding

Directional only

DeepSeek V3: 41.1; Estimated · #135/183
GPT-5.5: 67.7; Supported · #8/183
Basis: BenchAlign lane · 2 vs 9 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3: 40.7; Estimated · #136/181
GPT-5.5: 73.3; Supported · #7/181
Basis: BenchAlign lane · 2 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

DeepSeek V3: 39.6; #102/120
GPT-5.5: 92.9; #7/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

DeepSeek V3: 43.8; Unranked · 2 rankable rows
GPT-5.5: 63.5; #15/22
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3: 26.2; Unranked · 1 rankable row
GPT-5.5: 69.6; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3: Not ranked
GPT-5.5: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3: Not ranked
GPT-5.5: 71.3; #19/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V3 and GPT-5.5
Category	DeepSeek V3	GPT-5.5	Basis	Reading
Agentic	38.5Estimated · #124/151	63.9Supported · #15/151	Directional onlyBenchAlign lane · 0 vs 13 public rows	Directional only
Coding	41.1Estimated · #135/183	67.7Supported · #8/183	Directional onlyBenchAlign lane · 2 vs 9 public rows	Directional only
Knowledge	40.7Estimated · #136/181	73.3Supported · #7/181	Directional onlyBenchAlign lane · 2 vs 6 public rows	Directional only
Instruction following	39.6#102/120	92.9#7/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	43.8Unranked · 2 rankable rows	63.5#15/22	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Math	26.2Unranked · 1 rankable row	69.6Unranked · 3 rankable rows	Not comparableProvisional lane · 1 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

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.

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.

FrontierMath v2 (Tiers 1-3)
Math
DeepSeek V3: 1.724%GPT-5.5: 51.700%Normalized gap 50.0Shared source
GPQA
Knowledge
DeepSeek V3: 59.1%GPT-5.5: 93.6%Normalized gap 34.5DeepSeek V3 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 V3: $0.00082; Fits in one request
GPT-5.5: $0.02; Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3: $0.0168; Fits in one request
GPT-5.5: $0.34; Fits in one request

DeepSeek V3 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: $0.0304; Does not fit in one request
GPT-5.5: $0.5; Fits in one request

DeepSeek V3 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 V3GPT-5.5

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V3

128K

GPT-5.5

OpenAI pricing

API model ID

DeepSeek V3

Not sourced

GPT-5.5

gpt-5.5

OpenAI pricing

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V3

$0.07 per 1M cached input tokens

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

Documented inputs

DeepSeek V3

Not sourced

GPT-5.5

Not sourced

Documented outputs

DeepSeek V3

Not sourced

GPT-5.5

Not sourced

Provider availability

DeepSeek V3

Not sourced

GPT-5.5

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

GPT-5.5

Reasoning

Weight access

DeepSeek V3

Open Weight

GPT-5.5

Proprietary

License

DeepSeek V3

Open Weight

GPT-5.5

Proprietary

Release date

DeepSeek V3

2024-12-26

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 43.74, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.0168 vs $0.34. Cache-heavy agent loop: $0.0304 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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V3

API / mo$1,028

Self-host / mo$18,221

Break-even1.2B/day

GPT-5.5

API / mo$26,250

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence42 rows

Agentic

Terminal-Bench 2.0
DeepSeek V3—
GPT-5.582%
Source
Not directly comparable
CyberGym
DeepSeek V3—
GPT-5.581.8%
Source
Not directly comparable
BrowseComp
DeepSeek V3—
GPT-5.584.4%
Source
Not directly comparable
OSWorld-Verified
DeepSeek V3—
GPT-5.578.7%
Source
Not directly comparable
MCP Atlas
DeepSeek V3—
GPT-5.575.3%
Source
Not directly comparable
Toolathlon
DeepSeek V3—
GPT-5.555.6%
Source
Not directly comparable
τ²-bench results
DeepSeek V3—
GPT-5.598%
Source
Not directly comparable
Gert Labs
DeepSeek V3—
GPT-5.572.93%
Source
Not directly comparable
ResearchClawBench
DeepSeek V3—
GPT-5.517.0%
Source
Not directly comparable
OSWorld 2.0
DeepSeek V3—
GPT-5.513.0%
Source
Not directly comparable
JobBench
DeepSeek V3—
GPT-5.542.7%
Source
Not directly comparable
ExploitGym
DeepSeek V3—
GPT-5.513.4%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V3—
GPT-5.576.4%
Source
Not directly comparable

Coding

LiveCodeBench
DeepSeek V337.6%
Source
GPT-5.5—
Not directly comparable
SWE-bench Verified
DeepSeek V342%
Source
GPT-5.5—
Not directly comparable
SWE-bench Pro
DeepSeek V3—
GPT-5.558.6%
Source
Not directly comparable
Terminal-Bench 2.0
DeepSeek V3—
GPT-5.582.0%
Source
Not directly comparable
Vibe Code Bench
DeepSeek V3—
GPT-5.569.85%
Source
Not directly comparable
React Native Evals
DeepSeek V3—
GPT-5.584.7%
Source
Not directly comparable
cursorBench31
DeepSeek V3—
GPT-5.559.2%
Source
Not directly comparable
cursorBench32
DeepSeek V3—
GPT-5.558.4%
Source
Not directly comparable
FrontierCode 1.1 Main
DeepSeek V3—
GPT-5.543.0%
Source
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V3—
GPT-5.585.3%
Source
Not directly comparable
SWE-bench (Vals)
DeepSeek V3—
GPT-5.582.6%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
DeepSeek V3—
GPT-5.583.1%
Source
Not directly comparable
MRCR v2 128K-256K
DeepSeek V3—
GPT-5.587.5%
Source
Not directly comparable
ARC-AGI-2
DeepSeek V3—
GPT-5.585%
Source
Not directly comparable
ARC-AGI-3
DeepSeek V3—
GPT-5.50.4%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V359.1%
Source
GPT-5.593.6%
Source
GPT-5.5 leads this result
MMLU-Pro
DeepSeek V375.9%
Source
GPT-5.5—
Not directly comparable
GPQA-D
DeepSeek V3—
GPT-5.593.6%
Source
Not directly comparable
HLE
DeepSeek V3—
GPT-5.552.2%
Source
Not directly comparable
HLE w/o tools
DeepSeek V3—
GPT-5.541.4%
Source
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V3—
GPT-5.593.2%
Source
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V3—
GPT-5.588.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
DeepSeek V31.724%
GPT-5.551.700%
GPT-5.5 leads this result
FrontierMath (legacy)
DeepSeek V3—
GPT-5.551.7%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V3—
GPT-5.535.400%
Source
Not directly comparable

Multimodal

MMMU-Pro
DeepSeek V3—
GPT-5.581.2%
Source
Not directly comparable
MMMU-Pro w/ Python
DeepSeek V3—
GPT-5.583.2%
Source
Not directly comparable
OfficeQA Pro
DeepSeek V3—
GPT-5.554.1%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V386.1%
Source
GPT-5.5—
Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or GPT-5.5?

GPT-5.5 has the higher public score, 73.27 versus 43.74, 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 or GPT-5.5?

GPT-5.5 scores higher for coding on the public lane, 67.7 to 41.1. DeepSeek V3 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 or GPT-5.5?

GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 38.5. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V3 or GPT-5.5?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.02 on GPT-5.5; repository review costs $0.0168 and $0.34; the cache-heavy agent loop costs $0.0304 and $0.5. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or GPT-5.5?

GPT-5.5 has the larger documented context window: 1M, compared with 128K.

Related comparisons

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Last updated September 4, 2026

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DeepSeek V3 vs GPT-5.5

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Instruction following

Frequently asked questions

Which is better, DeepSeek V3 or GPT-5.5?

Which is better for coding, DeepSeek V3 or GPT-5.5?

Which is better for agentic tasks, DeepSeek V3 or GPT-5.5?

Which costs less, DeepSeek V3 or GPT-5.5?

Which has the larger context window, DeepSeek V3 or GPT-5.5?

Related comparisons

Watch DeepSeek V3 vs GPT-5.5