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Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

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Model A
GPT-5.4 mini

OpenAI

56.1/100

Estimated · Public rank #85

90% interval 44.6–67.6

GPT-5.4 mini vs Qwen3.5 Plus

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.5 Plus

Alibaba

46.7/100

Estimated · Public rank #147

90% interval 32.8–60.7

Decision reading

GPT-5.4 mini has the higher public score estimate, 56.08 versus 46.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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

    Qwen3.5 Plus

    Qwen3.5 Plus has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Qwen3.5 Plus

    Qwen3.5 Plus 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. Qwen3.5 Plus 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

    Qwen3.5 Plus

    Qwen3.5 Plus 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
3
GPT-5.4 mini only
12
Qwen3.5 Plus only
1
Like-for-like categories
1 / 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.

Math

Like-for-like
GPT-5.4 mini
21.7
Qwen3.5 Plus
16.3
Weighted basis
2 vs 2 rows
Reading
GPT-5.4 mini leads

Agentic

Not comparable
GPT-5.4 mini
65.7
Qwen3.5 Plus
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.4 mini
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 mini
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 mini
47.8
Qwen3.5 Plus
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 mini
Not measured
Qwen3.5 Plus
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.4 mini
76.6
Qwen3.5 Plus
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 mini
Not measured
Qwen3.5 Plus
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.

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.

  • FrontierMath v2 (Tiers 1-3)

    Math

    GPT-5.4 mini: 28.280%Qwen3.5 Plus: 21.034%Normalized gap 7.2Shared source
  • FrontierMath v2 (Tier 4)

    Math

    GPT-5.4 mini: 2.080%Qwen3.5 Plus: 2.083%Normalized gap 0.0Shared source

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
Qwen3.5 Plus
$0.0016
Fits in one request

Qwen3.5 Plus 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
Qwen3.5 Plus
$0.0272
Fits in one request

Qwen3.5 Plus 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
Qwen3.5 Plus
$0.112
Fits in one request
Cached input priced at the published list-input rate

GPT-5.4 mini has the lower modeled cost

Qwen3.5 Plus 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

Qwen3.5 Plus

Not published

Provider availability

GPT-5.4 mini

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.5 Plus

Not sourced

Reasoning profile

GPT-5.4 mini

Reasoning

Qwen3.5 Plus

Reasoning

Weight access

GPT-5.4 mini

Proprietary

Qwen3.5 Plus

Proprietary

License

GPT-5.4 mini

Proprietary

Qwen3.5 Plus

Proprietary

Release date

GPT-5.4 mini

2026-03-17

Qwen3.5 Plus

2026-03-04

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 mini has the higher public score estimate, 56.08 versus 46.73, but the 90% score intervals overlap.
Workload cost
Repository review: $0.051 vs $0.0272. Cache-heavy agent loop: $0.075 vs $0.112.
Context tradeoff
Qwen3.5 Plus 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 evidence16 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.4 mini60%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 mini72.1%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • MCP Atlas

    GPT-5.4 mini57.7%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • Toolathlon

    GPT-5.4 mini42.9%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • τ²-bench results

    GPT-5.4 mini93.4%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • JobBench

    GPT-5.4 mini
    Qwen3.5 Plus18.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    GPT-5.4 mini47.97%
    Qwen3.5 Plus15.74%

    GPT-5.4 mini leads this result

  • FrontierCode 1.1 Main

    GPT-5.4 mini27.0%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • EEBench

    GPT-5.4 mini18.3%
    Source
    Qwen3.5 Plus

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.4 mini88%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • HLE

    GPT-5.4 mini41.5%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 mini28.2%
    Source
    Qwen3.5 Plus

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-5.4 mini28.280%
    Qwen3.5 Plus21.034%

    GPT-5.4 mini leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GPT-5.4 mini2.080%
    Qwen3.5 Plus2.083%

    Qwen3.5 Plus leads this result

Multimodal

  • MMMU-Pro

    GPT-5.4 mini76.6%
    Source
    Qwen3.5 Plus

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.4 mini78%
    Source
    Qwen3.5 Plus

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 mini or Qwen3.5 Plus?

GPT-5.4 mini has the higher public score estimate, 56.08 versus 46.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.4 mini or Qwen3.5 Plus?

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 Qwen3.5 Plus?

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 Qwen3.5 Plus?

For the stated presets, chat costs $0.003 on GPT-5.4 mini and $0.0016 on Qwen3.5 Plus; repository review costs $0.051 and $0.0272; the cache-heavy agent loop costs $0.075 and $0.112. Qwen3.5 Plus 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 Qwen3.5 Plus?

Qwen3.5 Plus has the larger documented context window: 1M, compared with 400K.

Related comparisons

Last updated August 13, 2026

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