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Model comparison

Claude Fable 5 vs GPT-5.6 Terra

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

Claude Fable 5

Anthropic

82.8/100

Supported · Public rank #2

90% interval 79.8–85.7

GPT-5.6 Terra

OpenAI

72.3/100

Estimated · Public rank #12

90% interval 62.6–82.0

Claude Fable 5 has the higher public score estimate, 82.79 versus 72.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 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

    GPT-5.6 Terra

    GPT-5.6 Terra 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-5.6 Terra

    GPT-5.6 Terra 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-5.6 Terra

    GPT-5.6 Terra 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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
4
Claude Fable 5 only
7
GPT-5.6 Terra only
18
Like-for-like categories
0 / 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.

Agentic

Directional only
Claude Fable 5
84.6
GPT-5.6 Terra
87.4
Weighted basis
2 vs 2 rows
Reading
Directional only

Coding

Directional only
Claude Fable 5
89.2
GPT-5.6 Terra
63.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
Not measured
GPT-5.6 Terra
83.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5
Not measured
GPT-5.6 Terra
92.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
GPT-5.6 Terra
80.8
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not measured
GPT-5.6 Terra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
57.9
GPT-5.6 Terra
80.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
GPT-5.6 Terra
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

Claude Fable 5
$0.035
Fits in one request
GPT-5.6 Terra
$0.008
Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
GPT-5.6 Terra
$0.136
Fits in one request

GPT-5.6 Terra has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Fable 5
$0.9
Fits in one request
GPT-5.6 Terra
$0.2
Fits in one request

GPT-5.6 Terra 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.

Claude Fable 5

$1 per 1M cached input tokens

Claude API pricing

GPT-5.6 Terra

$0.2 per 1M cached input tokens

OpenAI pricing

Reasoning profile

Claude Fable 5

Reasoning

GPT-5.6 Terra

Reasoning

Weight access

Claude Fable 5

Proprietary

GPT-5.6 Terra

Proprietary

License

Claude Fable 5

Proprietary

GPT-5.6 Terra

Proprietary

Release date

Claude Fable 5

2026-06-09

GPT-5.6 Terra

2026-07-09

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
Claude Fable 5 has the higher public score estimate, 82.79 versus 72.29, but the 90% score intervals overlap.
Workload cost
Repository review: $0.65 vs $0.136. Cache-heavy agent loop: $0.9 vs $0.2.
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 evidence29 rows

Agentic

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • BrowseComp

    Claude Fable 5—
    GPT-5.6 Terra87.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5—
    GPT-5.6 Terra50.2%
    Source

    Not directly comparable

  • CyberGym

    Claude Fable 5—
    GPT-5.6 Terra81.8%
    Source

    Not directly comparable

  • ExploitGym

    Claude Fable 5—
    GPT-5.6 Terra23.2%
    Source

    Not directly comparable

  • Toolathlon

    Claude Fable 5—
    GPT-5.6 Terra53.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 580%
    Source
    GPT-5.6 Terra63.4%
    Source

    Claude Fable 5 leads this result

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GPT-5.6 Terra87.4%
    Source

    GPT-5.6 Terra leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • cursorBench32

    Shared source
    Claude Fable 570.5%
    GPT-5.6 Terra64.9%

    Claude Fable 5 leads this result

  • VulcanBench v3

    Claude Fable 587.0%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • deepSwe

    Claude Fable 5—
    GPT-5.6 Terra69.6%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Fable 5—
    GPT-5.6 Terra55.8%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude Fable 5—
    GPT-5.6 Terra83.9%
    Source

    Not directly comparable

  • ARC-AGI-3

    Claude Fable 5—
    GPT-5.6 Terra0.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Fable 5—
    GPT-5.6 Terra92.9%
    Source

    Not directly comparable

  • GPQA-D

    Claude Fable 5—
    GPT-5.6 Terra92.9%
    Source

    Not directly comparable

  • HealthBench Professional

    Claude Fable 5—
    GPT-5.6 Terra57.7%
    Source

    Not directly comparable

  • HealthBench Hard

    Claude Fable 5—
    GPT-5.6 Terra32.7%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Fable 5—
    GPT-5.6 Terra84.9%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Fable 5—
    GPT-5.6 Terra84.900%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Fable 5—
    GPT-5.6 Terra68.300%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    GPT-5.6 Terra—

    Not directly comparable

  • MMMU-Pro

    Claude Fable 5—
    GPT-5.6 Terra80.7%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Claude Fable 5—
    GPT-5.6 Terra82%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or GPT-5.6 Terra?

Claude Fable 5 has the higher public score estimate, 82.79 versus 72.29, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Fable 5 or GPT-5.6 Terra?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Fable 5 or GPT-5.6 Terra?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Fable 5 or GPT-5.6 Terra?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.008 on GPT-5.6 Terra; repository review costs $0.65 and $0.136; the cache-heavy agent loop costs $0.9 and $0.2. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5 or GPT-5.6 Terra?

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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