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DeepSeek-R1 vs GPT-5.4 nano

Updated October 29, 2026. Rank says GPT-5.4 nano is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GPT-5.4 nano has the higher public score estimate, 46.15 versus 40.4, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek-R1

DeepSeek

40.4/100

Supported · Public rank #138

90% interval 25.6–55.2

Model B
OpenAI logo
GPT-5.4 nano

OpenAI

46.15/100

Supported · Public rank #115

90% interval 34.8–57.5

Shared results
1
DeepSeek-R1 only
7
GPT-5.4 nano only
22
Like-for-like categories
1 / 8
Supported: DeepSeek-R1 and GPT-5.4 nanoHow the comparison works

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-5.4 nano

    GPT-5.4 nano has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 nano

    GPT-5.4 nano 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

    GPT-5.4 nano

    GPT-5.4 nano 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-R1 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    DeepSeek-R1 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-R1 does not fit this workload in one request.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—DeepSeek-R130.1GPT-5.4 nano

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Knowledge

Like-for-like
DeepSeek-R1
40.0
Supported · #115/179
GPT-5.4 nano
39.4
Supported · #117/179
Basis
BenchAlign v5.8 lane · 4 vs 5 public rows
Reading
DeepSeek-R1 leads · intervals overlap

Instruction following

Directional only
DeepSeek-R1
45.9
#110/157
GPT-5.4 nano
93.3
#12/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
DeepSeek-R1
Not ranked
GPT-5.4 nano
33.7
Supported · #97/135
Basis
BenchAlign v5.8 lane · 0 vs 6 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek-R1
Not ranked
GPT-5.4 nano
30.1
Supported · #100/156
Basis
BenchAlign v5.8 lane · 1 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek-R1
63.3
Unranked · 2 rankable rows
GPT-5.4 nano
40.6
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek-R1
Not ranked
GPT-5.4 nano
52.6
#51/58
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek-R1
Not ranked
GPT-5.4 nano
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek-R1
Not ranked
GPT-5.4 nano
52.3
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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-R1
$0.00165
Fits in one request
GPT-5.4 nano
$0.00082
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek-R1
$0.03407
Fits in one request
GPT-5.4 nano
$0.01375
Fits in one request

GPT-5.4 nano 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-R1
$0.0609
Does not fit in one request
GPT-5.4 nano
$0.0205
Fits in one request

DeepSeek-R1 does not fit this workload in one request.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

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

DeepSeek-R1

$0.14 per 1M cached input tokens

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

DeepSeek-R1

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

DeepSeek-R1

Reasoning

GPT-5.4 nano

Reasoning

Weight access

DeepSeek-R1

Open Weight

GPT-5.4 nano

Proprietary

License

DeepSeek-R1

Open Weight

GPT-5.4 nano

Proprietary

Release date

DeepSeek-R1

2025-01-20

GPT-5.4 nano

2026-03-17

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-5.4 nano has the higher public score estimate, 46.15 versus 40.4, but the 90% score intervals overlap.
Workload cost
Repository review: $0.03407 vs $0.01375. Cache-heavy agent loop: $0.0609 vs $0.0205.
Context tradeoff
GPT-5.4 nano has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek-R1 or GPT-5.4 nano?

GPT-5.4 nano has the higher public score estimate, 46.15 versus 40.4, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek-R1 or GPT-5.4 nano?

DeepSeek-R1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek-R1 or GPT-5.4 nano?

DeepSeek-R1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek-R1 or GPT-5.4 nano?

For the stated presets, chat costs $0.00164 on DeepSeek-R1 and $0.00082 on GPT-5.4 nano; repository review costs $0.03407 and $0.01375; the cache-heavy agent loop costs $0.0609 and $0.0205. DeepSeek-R1 does not fit this workload in one request.

Which has the larger context window, DeepSeek-R1 or GPT-5.4 nano?

GPT-5.4 nano has the larger documented context window: 400K, compared with 128K.

Self-host vs API cost

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

DeepSeek-R1
API / mo$2,055
Self-host / mo$27,962
Break-even894M/day
GPT-5.4 nano
API / mo$1,088
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 evidence30 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek-R1—
    GPT-5.4 nano46.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek-R1—
    GPT-5.4 nano39%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek-R1—
    GPT-5.4 nano56.1%
    Source

    Not directly comparable

  • Toolathlon

    DeepSeek-R1—
    GPT-5.4 nano35.5%
    Source

    Not directly comparable

  • τ²-bench results

    DeepSeek-R1—
    GPT-5.4 nano92.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek-R1—
    GPT-5.4 nano41.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    DeepSeek-R149.2%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek-R1—
    GPT-5.4 nano46.3%
    Source

    Not directly comparable

  • Vibe Code Bench

    DeepSeek-R1—
    GPT-5.4 nano26.10%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek-R1—
    GPT-5.4 nano84.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek-R1—
    GPT-5.4 nano69.8%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    DeepSeek-R1—
    GPT-5.4 nano51.50%
    Source

    Not directly comparable

  • ARC-AGI-2

    DeepSeek-R1—
    GPT-5.4 nano5.7%
    Source

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    DeepSeek-R1—
    GPT-5.4 nano74.90%
    Source

    Not directly comparable

  • ParseBench

    DeepSeek-R1—
    GPT-5.4 nano43.35%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek-R1—
    GPT-5.4 nano66.1%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    DeepSeek-R1—
    GPT-5.4 nano69.5%
    Source

    Not directly comparable

Knowledge

  • MMLU

    DeepSeek-R190.8%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • GPQA

    DeepSeek-R171.5%
    Source
    GPT-5.4 nano82.8%
    Source

    GPT-5.4 nano leads this result

  • MMLU-Pro

    DeepSeek-R184%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • SimpleQA

    DeepSeek-R130.1%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • HLE

    DeepSeek-R1—
    GPT-5.4 nano37.7%
    Source

    Not directly comparable

  • HLE w/o tools

    DeepSeek-R1—
    GPT-5.4 nano24.3%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek-R1—
    GPT-5.4 nano77.5%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek-R1—
    GPT-5.4 nano77.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek-R183.3%
    Source
    GPT-5.4 nano—

    Not directly comparable

Math

  • AIME 2024

    DeepSeek-R179.8%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • MATH-500

    DeepSeek-R197.3%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek-R1—
    GPT-5.4 nano44.912%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek-R1—
    GPT-5.4 nano12.195%
    Source

    Not directly comparable

30 public results · 1 shared

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Last updated October 29, 2026