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BenchLM

Composer 2 vs GPT-5.4 mini

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.

Cursor logo
Model A
Composer 2

Cursor

—

Evidence status unavailable

90% interval unavailable

OpenAI logo
Model B
GPT-5.4 mini

OpenAI

61.12/100

Supported · Public rank #62

90% interval 50.6–71.7

Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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-5.4 mini

    GPT-5.4 mini has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Composer 2

    Composer 2 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

    Composer 2

    Composer 2 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

    Composer 2 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

    Composer 2 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. Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

49.5Composer 243.3GPT-5.4 mini

Directional only · BenchAlign

Composer 2 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

Open the coding leaderboardShortlist models for this job

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
Composer 2 only
4
GPT-5.4 mini only
18
Like-for-like categories
0 / 8

2 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
Composer 2
49.5
Estimated · #64/154
GPT-5.4 mini
39.2
Supported · #123/154
Basis
BenchAlign lane · 1 vs 6 public rows
Reading
Directional only

Coding

Directional only
Composer 2
49.5
Estimated · #68/156
GPT-5.4 mini
43.3
Supported · #105/156
Basis
BenchAlign lane · 4 vs 4 public rows
Reading
Directional only

Reasoning

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

Multimodal

Not comparable
Composer 2
Not ranked
GPT-5.4 mini
57.3
#32/49
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Composer 2
Not ranked
GPT-5.4 mini
55.7
Supported · #52/186
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Not comparable

Multilingual

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

Instruction following

Not comparable
Composer 2
Not ranked
GPT-5.4 mini
88.5
#24/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Composer 2
Not ranked
GPT-5.4 mini
44.3
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) 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.

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

Composer 2
$0.00175
Fits in one request
GPT-5.4 mini
$0.003
Fits in one request

Composer 2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Composer 2
$0.0325
Fits in one request
GPT-5.4 mini
$0.051
Fits in one request

Composer 2 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Composer 2
$0.135
Does not fit in one request
Cached input priced at the published list-input rate
GPT-5.4 mini
$0.075
Fits in one request

Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input 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.

Composer 2

Not published

GPT-5.4 mini

$0.075 per 1M cached input tokens

OpenAI pricing

Provider availability

Composer 2

Not sourced

GPT-5.4 mini

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Composer 2

Reasoning

GPT-5.4 mini

Reasoning

Weight access

Composer 2

Proprietary

GPT-5.4 mini

Proprietary

License

Composer 2

Proprietary

GPT-5.4 mini

Proprietary

Release date

Composer 2

2026-03-19

GPT-5.4 mini

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
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: $0.0325 vs $0.051. Cache-heavy agent loop: $0.135 vs $0.075.
Context tradeoff
GPT-5.4 mini has the larger documented window (400K).

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

    Composer 261.7%
    Source
    GPT-5.4 mini60%
    Source

    Composer 2 leads this result

  • OSWorld-Verified

    Composer 2—
    GPT-5.4 mini72.1%
    Source

    Not directly comparable

  • MCP Atlas

    Composer 2—
    GPT-5.4 mini57.7%
    Source

    Not directly comparable

  • Toolathlon

    Composer 2—
    GPT-5.4 mini42.9%
    Source

    Not directly comparable

  • τ²-bench results

    Composer 2—
    GPT-5.4 mini93.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Composer 2—
    GPT-5.4 mini54.7%
    Source

    Not directly comparable

Coding

  • SWE Multilingual

    Composer 273.7%
    Source
    GPT-5.4 mini—

    Not directly comparable

  • SWE-Rebench

    Composer 258%
    Source
    GPT-5.4 mini—

    Not directly comparable

  • React Native Evals

    Composer 296.1%
    Source
    GPT-5.4 mini—

    Not directly comparable

  • Terminal-Bench 2.0

    Composer 261.7%
    Source
    GPT-5.4 mini—

    Not directly comparable

  • Vibe Code Bench

    Composer 2—
    GPT-5.4 mini47.97%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    Composer 2—
    GPT-5.4 mini27.0%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Composer 2—
    GPT-5.4 mini81.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Composer 2—
    GPT-5.4 mini73.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Composer 2—
    GPT-5.4 mini76.6%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Composer 2—
    GPT-5.4 mini78%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Composer 2—
    GPT-5.4 mini88%
    Source

    Not directly comparable

  • HLE

    Composer 2—
    GPT-5.4 mini41.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Composer 2—
    GPT-5.4 mini28.2%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Composer 2—
    GPT-5.4 mini83.1%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Composer 2—
    GPT-5.4 mini84.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Composer 2—
    GPT-5.4 mini28.280%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Composer 2—
    GPT-5.4 mini2.080%
    Source

    Not directly comparable

Questions

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

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, Composer 2 or GPT-5.4 mini?

Composer 2 scores higher for coding on the public lane, 49.5 to 43.3. Composer 2 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, Composer 2 or GPT-5.4 mini?

Composer 2 scores higher for agentic tasks on the public lane, 49.5 to 39.2. Composer 2 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, Composer 2 or GPT-5.4 mini?

For the stated presets, chat costs $0.00175 on Composer 2 and $0.003 on GPT-5.4 mini; repository review costs $0.0325 and $0.051; the cache-heavy agent loop costs $0.135 and $0.075. Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate.

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

GPT-5.4 mini has the larger documented context window: 400K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 21, 2026

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