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Model comparison

GPT-5.4 mini vs Step 3.5 Flash

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.4 mini

OpenAI

55.8/100

Estimated · Public rank #81

90% interval 44.3–67.3

Step 3.5 Flash

StepFun

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

  • 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

    Step 3.5 Flash

    Step 3.5 Flash 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

    Step 3.5 Flash

    Step 3.5 Flash has the lower estimated token cost for this stated workload. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Step 3.5 Flash

    Step 3.5 Flash 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

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
0
GPT-5.4 mini only
14
Step 3.5 Flash only
0
Like-for-like categories
0 / 8

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.

Agentic

Not comparable
GPT-5.4 mini
65.7
Step 3.5 Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.4 mini
Not measured
Step 3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 mini
Not measured
Step 3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 mini
47.8
Step 3.5 Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 mini
21.7
Step 3.5 Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 mini
Not measured
Step 3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 mini
76.6
Step 3.5 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 mini
Not measured
Step 3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

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
Step 3.5 Flash
$0.00025
Fits in one request

Step 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 mini
$0.051
Fits in one request
Step 3.5 Flash
$0.0059
Fits in one request

Step 3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.4 mini
$0.075
Fits in one request
Step 3.5 Flash
$0.025
Fits in one request
Cached input priced at the published list-input rate

Step 3.5 Flash has the lower modeled cost

Step 3.5 Flash 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.

GPT-5.4 mini

$0.075 per 1M cached input tokens

OpenAI pricing

Step 3.5 Flash

Not published

Provider availability

GPT-5.4 mini

Generally Available · OpenAI Responses API

OpenAI model catalog

Step 3.5 Flash

Not sourced

Reasoning profile

GPT-5.4 mini

Reasoning

Step 3.5 Flash

Non-Reasoning

Weight access

GPT-5.4 mini

Proprietary

Step 3.5 Flash

Open Weight

License

GPT-5.4 mini

Proprietary

Step 3.5 Flash

Open Weight

Release date

GPT-5.4 mini

2026-03-17

Step 3.5 Flash

2026-01-20

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.051 vs $0.0059. Cache-heavy agent loop: $0.075 vs $0.025.
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 evidence14 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 mini60%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 mini72.1%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 mini57.7%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • Toolathlon

    GPT-5.4 mini42.9%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 mini93.4%
    Source
    Step 3.5 Flash—

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-5.4 mini47.97%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • FrontierCode 1.1 Main

    GPT-5.4 mini27.0%
    Source
    Step 3.5 Flash—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 mini88%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • HLE

    GPT-5.4 mini41.5%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 mini28.2%
    Source
    Step 3.5 Flash—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 mini28.280%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 mini2.080%
    Source
    Step 3.5 Flash—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 mini76.6%
    Source
    Step 3.5 Flash—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.4 mini78%
    Source
    Step 3.5 Flash—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 mini or Step 3.5 Flash?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.4 mini or Step 3.5 Flash?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-5.4 mini or Step 3.5 Flash?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-5.4 mini or Step 3.5 Flash?

For the stated presets, chat costs $0.003 on GPT-5.4 mini and $0.00025 on Step 3.5 Flash; repository review costs $0.051 and $0.0059; the cache-heavy agent loop costs $0.075 and $0.025. Step 3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.4 mini or Step 3.5 Flash?

GPT-5.4 mini has the larger documented context window: 400K, compared with 256K.

Related comparisons

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Last updated July 30, 2026

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