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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-5.2

OpenAI

58.2/100

Estimated · Public rank #78

90% interval 50.0–66.4

GPT-5.2 vs Qwen3.6 Plus

Updated August 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.6 Plus

Alibaba

64.8/100

Supported · Public rank #36

90% interval 55.7–73.9

Decision reading

Qwen3.6 Plus has the higher public score estimate, 64.79 versus 58.24, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

11 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-5.2

    GPT-5.2 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.6 Plus

    Qwen3.6 Plus has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • 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

  • 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: rate-fallback

  • 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
11
GPT-5.2 only
4
Qwen3.6 Plus only
33
Like-for-like categories
2 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Like-for-like
GPT-5.2
70.6
Qwen3.6 Plus
70.3
Weighted basis
2 vs 2 rows
Reading
GPT-5.2 leads

Multimodal

Like-for-like
GPT-5.2
80.4
Qwen3.6 Plus
79.8
Weighted basis
2 vs 2 rows
Reading
GPT-5.2 leads

Knowledge

Directional only
GPT-5.2
92.4
Qwen3.6 Plus
57.1
Weighted basis
1 vs 4 rows
Reading
Directional only

Math

Directional only
GPT-5.2
35.2
Qwen3.6 Plus
60.5
Weighted basis
2 vs 4 rows
Reading
Directional only

Agentic

Not comparable
GPT-5.2
55.7
Qwen3.6 Plus
61.6
Weighted basis
2 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.2
52.9
Qwen3.6 Plus
62.0
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.2
Not measured
Qwen3.6 Plus
84.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.2
Not measured
Qwen3.6 Plus
82.3
Weighted basis
0 vs 2 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.

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-5.2
$0.00875
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.2
$0.1295
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.2
$0.525
Fits in one request
Cached input priced at the published list-input rate
Qwen3.6 Plus
API rate not published
Fits in one request
Cached-input rate unavailable

GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus 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.

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.2

400K

Qwen3.6 Plus

1M

API model ID

GPT-5.2

Not sourced

Qwen3.6 Plus

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.2

Not published

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

GPT-5.2

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

GPT-5.2

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

GPT-5.2

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

GPT-5.2

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

GPT-5.2

Proprietary

Qwen3.6 Plus

Proprietary

License

GPT-5.2

Proprietary

Qwen3.6 Plus

Proprietary

Release date

GPT-5.2

2025-12-11

Qwen3.6 Plus

2026-04-02

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
Qwen3.6 Plus has the higher public score estimate, 64.79 versus 58.24, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.6 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 evidence48 rows

Agentic

  • BrowseComp

    GPT-5.265.8%
    Source
    Qwen3.6 Plus

    Not directly comparable

  • OSWorld-Verified

    GPT-5.247.3%
    Source
    Qwen3.6 Plus

    Not directly comparable

  • GPT-5.246.54%
    Qwen3.6 Plus50.60%

    Qwen3.6 Plus leads this result

  • JobBench

    GPT-5.234.3%
    Source
    Qwen3.6 Plus

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.2
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.2
    Qwen3.6 Plus58.8%
    Source

    Not directly comparable

  • QwenClawBench

    GPT-5.2
    Qwen3.6 Plus57.2%
    Source

    Not directly comparable

  • τ³-bench results

    GPT-5.2
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    GPT-5.2
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    GPT-5.2
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    GPT-5.2
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-5.2
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    GPT-5.2
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    GPT-5.2
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • ResearchClawBench

    GPT-5.2
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.280%
    Source
    Qwen3.6 Plus78.8%
    Source

    GPT-5.2 leads this result

  • SWE-bench Pro

    GPT-5.255.6%
    Source
    Qwen3.6 Plus56.6%
    Source

    Qwen3.6 Plus leads this result

  • Vibe Code Bench

    Shared source
    GPT-5.253.50%
    Qwen3.6 Plus25.56%

    GPT-5.2 leads this result

  • SWE Multilingual

    GPT-5.2
    Qwen3.6 Plus73.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    GPT-5.2
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • EEBench

    GPT-5.2
    Qwen3.6 Plus12.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.252.9%
    Source
    Qwen3.6 Plus

    Not directly comparable

  • AI-Needle

    GPT-5.2
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    GPT-5.2
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.292.4%
    Source
    Qwen3.6 Plus90.4%
    Source

    GPT-5.2 leads this result

  • SuperGPQA

    GPT-5.2
    Qwen3.6 Plus71.6%
    Source

    Not directly comparable

  • MMLU-Pro

    GPT-5.2
    Qwen3.6 Plus88.5%
    Source

    Not directly comparable

  • MMLU-Redux

    GPT-5.2
    Qwen3.6 Plus94.5%
    Source

    Not directly comparable

  • C-Eval

    GPT-5.2
    Qwen3.6 Plus93.3%
    Source

    Not directly comparable

  • HLE

    GPT-5.2
    Qwen3.6 Plus28.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GPT-5.240.700%
    Qwen3.6 Plus26.207%

    GPT-5.2 leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GPT-5.218.800%
    Qwen3.6 Plus8.333%

    GPT-5.2 leads this result

  • AIME26

    GPT-5.2
    Qwen3.6 Plus95.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    GPT-5.2
    Qwen3.6 Plus96.7%
    Source

    Not directly comparable

  • HMMT Nov 2025

    GPT-5.2
    Qwen3.6 Plus94.6%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-5.2
    Qwen3.6 Plus87.8%
    Source

    Not directly comparable

  • MMAnswerBench

    GPT-5.2
    Qwen3.6 Plus83.8%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    GPT-5.2
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    GPT-5.2
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.279.5%
    Source
    Qwen3.6 Plus78.8%
    Source

    GPT-5.2 leads this result

  • MathVision

    Shared source
    GPT-5.283.0%
    Qwen3.6 Plus88.0%

    Qwen3.6 Plus leads this result

  • GPT-5.282.1%
    Qwen3.6 Plus81.5%

    GPT-5.2 leads this result

  • GPT-5.275.9%
    Qwen3.6 Plus96.9%

    Qwen3.6 Plus leads this result

  • MMMU

    GPT-5.2
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • VideoMMMU

    GPT-5.2
    Qwen3.6 Plus84.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    GPT-5.2
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    GPT-5.2
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    GPT-5.2
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public score estimate, 64.79 versus 58.24, 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-5.2 or Qwen3.6 Plus?

GPT-5.2 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, GPT-5.2 or Qwen3.6 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-5.2 or Qwen3.6 Plus?

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-5.2 or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 400K.

Related comparisons

Last updated August 15, 2026

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