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Model comparison

GPT-5.6 Terra vs Qwen3.5 Flash

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Terra

OpenAI

72.3/100

Estimated · Public rank #12

90% interval 62.6–82.0

Qwen3.5 Flash

Alibaba

46.9/100

Supported · Public rank #141

90% interval 24.2–69.6

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 46.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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-5.6 Terra

    GPT-5.6 Terra has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Qwen3.5 Flash

    Qwen3.5 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

    Qwen3.5 Flash

    Qwen3.5 Flash has the lower estimated token cost for this stated workload. Qwen3.5 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

    Qwen3.5 Flash

    Qwen3.5 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
2
GPT-5.6 Terra only
20
Qwen3.5 Flash only
0
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-5.6 Terra
80.8
Qwen3.5 Flash
4.7
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Terra leads

Agentic

Not comparable
GPT-5.6 Terra
87.4
Qwen3.5 Flash
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Terra
63.4
Qwen3.5 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Qwen3.5 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Terra
92.9
Qwen3.5 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Qwen3.5 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Qwen3.5 Flash
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Qwen3.5 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.

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-5.6 Terra
$0.008
Fits in one request
Qwen3.5 Flash
$0.0003
Fits in one request

Qwen3.5 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Terra
$0.136
Fits in one request
Qwen3.5 Flash
$0.0062
Fits in one request

Qwen3.5 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-5.6 Terra
$0.2
Fits in one request
Qwen3.5 Flash
$0.026
Fits in one request
Cached input priced at the published list-input rate

Qwen3.5 Flash has the lower modeled cost

Qwen3.5 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.

Context window

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

GPT-5.6 Terra

1.05M

OpenAI model catalog

Qwen3.5 Flash

1M

Cached-input rate

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

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Qwen3.5 Flash

Not published

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Qwen3.5 Flash

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Qwen3.5 Flash

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Qwen3.5 Flash

Proprietary

License

GPT-5.6 Terra

Proprietary

Qwen3.5 Flash

Proprietary

Release date

GPT-5.6 Terra

2026-07-09

Qwen3.5 Flash

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-5.6 Terra has the higher public score estimate, 72.29 versus 46.9, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.0062. Cache-heavy agent loop: $0.2 vs $0.026.
Context tradeoff
GPT-5.6 Terra has the larger documented window (1.05M).

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 evidence22 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Terra64.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Qwen3.5 Flash6.207%
    Source

    GPT-5.6 Terra leads this result

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Qwen3.5 Flash0.000%
    Source

    GPT-5.6 Terra leads this result

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Qwen3.5 Flash—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Qwen3.5 Flash—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Qwen3.5 Flash?

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 46.9, 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-5.6 Terra or Qwen3.5 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-5.6 Terra or Qwen3.5 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-5.6 Terra or Qwen3.5 Flash?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.0003 on Qwen3.5 Flash; repository review costs $0.136 and $0.0062; the cache-heavy agent loop costs $0.2 and $0.026. Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.6 Terra or Qwen3.5 Flash?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

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Last updated August 10, 2026

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