Skip to main content
Radar

Every change to the models you run, with its source and its date. Releases, price changes, retirements, API changes, and incidents.Every change to the models you run, with its source.

Follow model changes
BenchLM

GPT-5.4 mini vs Pareto 26.9

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.

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

OpenAI logo
Model A
GPT-5.4 mini

OpenAI

61.05/100

Supported · Public rank #59

90% interval 50.8–71.3

Model B
Pareto 26.9

Unbiased

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export

Share on XLinkedInSocial cardCSVJSON

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 mini

    GPT-5.4 mini 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

    GPT-5.4 mini

    GPT-5.4 mini 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 mini

    GPT-5.4 mini 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

    Pareto 26.9 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

    Pareto 26.9 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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
2
GPT-5.4 mini only
17
Pareto 26.9 only
2
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 · #124/154
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 6 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
GPT-5.4 mini
42.6
Supported · #106/154
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 mini
73.9
Unranked · 2 rankable rows
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 mini
55.6
Supported · #52/184
Pareto 26.9
Not ranked
Basis
BenchAlign lane · 5 vs 1 public rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 mini
44.3
Unranked · 2 rankable rows
Pareto 26.9
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 mini
Not ranked
Pareto 26.9
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 mini
57.3
#31/48
Pareto 26.9
Not ranked
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 mini
88.5
#24/124
Pareto 26.9
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.

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

GPT-5.4 mini
$0.003
Fits in one request
Pareto 26.9
$0.00625
Fit state unavailable

GPT-5.4 mini 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
Pareto 26.9
$0.1475
Fit state unavailable

GPT-5.4 mini 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
Pareto 26.9
$0.175
Fit state unavailable

GPT-5.4 mini has the lower modeled cost

Costs use the listed standard API rates.

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

Pareto 26.9

$0.25 per 1M cached input tokens

Unbiased pricing

Provider availability

GPT-5.4 mini

Generally Available · OpenAI Responses API

OpenAI model catalog

Pareto 26.9

Not sourced

Reasoning profile

GPT-5.4 mini

Reasoning

Pareto 26.9

Reasoning

Weight access

GPT-5.4 mini

Proprietary

Pareto 26.9

Proprietary

License

GPT-5.4 mini

Proprietary

Pareto 26.9

Proprietary

Release date

GPT-5.4 mini

2026-03-17

Pareto 26.9

2026-09-17

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: $0.051 vs $0.1475. Cache-heavy agent loop: $0.075 vs $0.175.
Context tradeoff
A complete documented context comparison is not available.

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 evidence21 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 mini60%
    Source
    Pareto 26.9—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 mini72.1%
    Source
    Pareto 26.9—

    Not directly comparable

  • MCP Atlas

    GPT-5.4 mini57.7%
    Source
    Pareto 26.9—

    Not directly comparable

  • Toolathlon

    GPT-5.4 mini42.9%
    Source
    Pareto 26.9—

    Not directly comparable

  • τ²-bench results

    GPT-5.4 mini93.4%
    Source
    Pareto 26.9—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.4 mini54.7%
    Source
    Pareto 26.9—

    Not directly comparable

  • Terminal-Bench 4.0

    GPT-5.4 mini—
    Pareto 26.951.00%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    GPT-5.4 mini47.97%
    Source
    Pareto 26.9—

    Not directly comparable

  • FrontierCode 1.1 Main

    GPT-5.4 mini27.0%
    Source
    Pareto 26.9—

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.4 mini81.5%
    Source
    Pareto 26.9—

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.4 mini73.0%
    Source
    Pareto 26.9—

    Not directly comparable

  • DeepSWE

    GPT-5.4 mini—
    Pareto 26.974.0%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 mini88%
    Source
    Pareto 26.9—

    Not directly comparable

  • HLE

    GPT-5.4 mini41.5%
    Source
    Pareto 26.9—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 mini28.2%
    Source
    Pareto 26.949%
    Source

    Pareto 26.9 leads this result

  • GPQA Diamond (Vals)

    GPT-5.4 mini83.1%
    Source
    Pareto 26.9—

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.4 mini84.6%
    Source
    Pareto 26.9—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 mini28.280%
    Source
    Pareto 26.9—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 mini2.080%
    Source
    Pareto 26.9—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 mini76.6%
    Source
    Pareto 26.978%
    Source

    Pareto 26.9 leads this result

  • MMMU-Pro w/ Python

    GPT-5.4 mini78%
    Source
    Pareto 26.9—

    Not directly comparable

Questions

Which is better, GPT-5.4 mini or Pareto 26.9?

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 Pareto 26.9?

Pareto 26.9 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 Pareto 26.9?

Pareto 26.9 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 Pareto 26.9?

For the stated presets, chat costs $0.003 on GPT-5.4 mini and $0.00625 on Pareto 26.9; repository review costs $0.051 and $0.1475; the cache-heavy agent loop costs $0.075 and $0.175. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.4 mini or Pareto 26.9?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

Watch GPT-5.4 mini vs Pareto 26.9

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.