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Model comparison

GPT-5.6 Terra vs Ling 3.0 Flash FP8

Updated August 4, 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 62.5–82.0

Ling 3.0 Flash FP8

InclusionAI

—

Evidence status unavailable

90% interval unavailable

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 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

Show secondary and unsupported calls

  • 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

  • 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: listed-rates

  • 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
2
GPT-5.6 Terra only
20
Ling 3.0 Flash FP8 only
2
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.

Knowledge

Like-for-like
GPT-5.6 Terra
92.9
Ling 3.0 Flash FP8
84.0
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Terra leads

Agentic

Not comparable
GPT-5.6 Terra
87.4
Ling 3.0 Flash FP8
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Terra
63.4
Ling 3.0 Flash FP8
40.4
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Ling 3.0 Flash FP8
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Terra
80.8
Ling 3.0 Flash FP8
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Ling 3.0 Flash FP8
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Ling 3.0 Flash FP8
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Ling 3.0 Flash FP8
73.4
Weighted basis
0 vs 1 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
Ling 3.0 Flash FP8
API rate not published
Fits in one request

Ling 3.0 Flash FP8 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Terra
$0.136
Fits in one request
Ling 3.0 Flash FP8
API rate not published
Fits in one request

Ling 3.0 Flash FP8 has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.6 Terra
$0.2
Fits in one request
Ling 3.0 Flash FP8
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash FP8 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.

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Ling 3.0 Flash FP8

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Ling 3.0 Flash FP8

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Ling 3.0 Flash FP8

Open Weight

License

GPT-5.6 Terra

Proprietary

Ling 3.0 Flash FP8

Open Weight

Release date

GPT-5.6 Terra

2026-07-09

Ling 3.0 Flash FP8

2026-08-04

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
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
A complete comparable API-rate estimate is not available for both models.
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 evidence24 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Terra64.9%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • SciCode

    GPT-5.6 Terra—
    Ling 3.0 Flash FP840.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Ling 3.0 Flash FP884%
    Source

    GPT-5.6 Terra leads this result

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Ling 3.0 Flash FP884.0%
    Source

    GPT-5.6 Terra leads this result

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Instruction following

  • IFBench

    GPT-5.6 Terra—
    Ling 3.0 Flash FP873.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Ling 3.0 Flash FP8?

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-5.6 Terra or Ling 3.0 Flash FP8?

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 Ling 3.0 Flash FP8?

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 Ling 3.0 Flash FP8?

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-5.6 Terra or Ling 3.0 Flash FP8?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

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Last updated August 4, 2026

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