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BenchLM
OpenAI logo
Model A
GPT-5.4 mini

OpenAI

61.11/100

Supported · Public rank #59

90% interval 51.0–71.2

GPT-5.4 mini vs SWE-2

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Cognition logo
Model B
SWE-2

Cognition

—

Evidence status unavailable

90% interval unavailable

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.

1 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

    SWE-2

    SWE-2 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    SWE-2 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

    SWE-2 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    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.

Shared results
1
GPT-5.4 mini only
18
SWE-2 only
4
Like-for-like categories
0 / 8

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
GPT-5.4 mini
39.1
Estimated · #119/152
SWE-2
Not ranked
Basis
BenchAlign lane · 6 vs 2 public rows
Reading
Not comparable

Coding

Not comparable
GPT-5.4 mini
42.7
Supported · #104/151
SWE-2
Not ranked
Basis
BenchAlign lane · 4 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 mini
73.9
Unranked · 2 rankable rows
SWE-2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 mini
55.6
Supported · #52/183
SWE-2
Not ranked
Basis
BenchAlign lane · 5 vs 0 public rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 mini
44.5
Unranked · 2 rankable rows
SWE-2
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 mini
Not ranked
SWE-2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 mini
57.2
#31/48
SWE-2
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 mini
89.8
#23/123
SWE-2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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

GPT-5.4 mini
$0.003
Fits in one request
SWE-2
API rate not published
Fits in one request

SWE-2 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 mini
$0.051
Fits in one request
SWE-2
API rate not published
Fits in one request

SWE-2 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.4 mini
$0.075
Fits in one request
SWE-2
API rate not published
Fits in one request
Cached-input rate unavailable

SWE-2 has no comparable published API token rate.

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.

GPT-5.4 mini

$0.075 per 1M cached input tokens

OpenAI pricing

SWE-2

No comparable hosted API rate

Cognition SWE-2 launch post

Provider availability

GPT-5.4 mini

Generally Available · OpenAI Responses API

OpenAI model catalog

SWE-2

Not sourced

Reasoning profile

GPT-5.4 mini

Reasoning

SWE-2

Reasoning

Weight access

GPT-5.4 mini

Proprietary

SWE-2

Proprietary

License

GPT-5.4 mini

Proprietary

SWE-2

Proprietary

Release date

GPT-5.4 mini

2026-03-17

SWE-2

2026-09-10

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
SWE-2 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 evidence23 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 mini60%
    Source
    SWE-2—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 mini72.1%
    Source
    SWE-2—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 mini57.7%
    Source
    SWE-2—

    Not directly comparable

  • Toolathlon

    GPT-5.4 mini42.9%
    Source
    SWE-2—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 mini93.4%
    Source
    SWE-2—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.4 mini54.7%
    Source
    SWE-2—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.4 mini—
    SWE-292.8%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    GPT-5.4 mini—
    SWE-227.30%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-5.4 mini47.97%
    Source
    SWE-2—

    Not directly comparable

  • FrontierCode 1.1 Main

    GPT-5.4 mini27.0%
    Source
    SWE-250.0%
    Source

    SWE-2 leads this result

  • LiveCodeBench (Vals)

    GPT-5.4 mini81.5%
    Source
    SWE-2—

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.4 mini73.0%
    Source
    SWE-2—

    Not directly comparable

  • Terminal-Bench 2.1

    GPT-5.4 mini—
    SWE-292.8%
    Source

    Not directly comparable

  • DeepSWE

    GPT-5.4 mini—
    SWE-273.0%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 mini88%
    Source
    SWE-2—

    Not directly comparable

  • HLE

    GPT-5.4 mini41.5%
    Source
    SWE-2—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 mini28.2%
    Source
    SWE-2—

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.4 mini83.1%
    Source
    SWE-2—

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.4 mini84.6%
    Source
    SWE-2—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 mini28.280%
    Source
    SWE-2—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 mini2.080%
    Source
    SWE-2—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 mini76.6%
    Source
    SWE-2—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.4 mini78%
    Source
    SWE-2—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 mini or SWE-2?

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, GPT-5.4 mini or SWE-2?

SWE-2 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.4 mini or SWE-2?

SWE-2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.4 mini or SWE-2?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.4 mini or SWE-2?

SWE-2 has the larger documented context window: 1M, compared with 400K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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