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Model comparison

GPT-5.6 Terra vs Kimi K2.7 Code

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

Kimi K2.7 Code

Moonshot AI

54.1/100

Estimated · Public rank #95

90% interval 42.2–65.9

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 54.07, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    Kimi K2.7 Code

    Kimi K2.7 Code 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. Kimi K2.7 Code 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

    Kimi K2.7 Code

    Kimi K2.7 Code 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
1
GPT-5.6 Terra only
21
Kimi K2.7 Code only
6
Like-for-like categories
0 / 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.

Agentic

Not comparable
GPT-5.6 Terra
87.4
Kimi K2.7 Code
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Terra
63.4
Kimi K2.7 Code
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Kimi K2.7 Code
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Terra
92.9
Kimi K2.7 Code
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Terra
80.8
Kimi K2.7 Code
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Kimi K2.7 Code
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Kimi K2.7 Code
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Kimi K2.7 Code
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
Kimi K2.7 Code
$0.00295
Fits in one request

Kimi K2.7 Code 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
Kimi K2.7 Code
$0.0595
Fits in one request

Kimi K2.7 Code 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
Kimi K2.7 Code
$0.249
Fits in one request
Cached input priced at the published list-input rate

GPT-5.6 Terra has the lower modeled cost

Kimi K2.7 Code 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

Kimi K2.7 Code

256K

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

Kimi K2.7 Code

Not published

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Kimi K2.7 Code

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Kimi K2.7 Code

Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Kimi K2.7 Code

Open Weight

License

GPT-5.6 Terra

Proprietary

Kimi K2.7 Code

Open Weight

Release date

GPT-5.6 Terra

2026-07-09

Kimi K2.7 Code

2026-06-12

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 54.07, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.0595. Cache-heavy agent loop: $0.2 vs $0.249.
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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-5.6 Terra
API / mo$10,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence28 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • Kimi Claw 24/7

    GPT-5.6 Terra—
    Kimi K2.7 Code46.9%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-5.6 Terra—
    Kimi K2.7 Code76%
    Source

    Not directly comparable

  • MCP Mark Verified

    GPT-5.6 Terra—
    Kimi K2.7 Code81.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • cursorBench32

    Shared source
    GPT-5.6 Terra64.9%
    Kimi K2.7 Code49.7%

    GPT-5.6 Terra leads this result

  • Kimi Code Bench v2

    GPT-5.6 Terra—
    Kimi K2.7 Code62.0%
    Source

    Not directly comparable

  • ProgramBench

    GPT-5.6 Terra—
    Kimi K2.7 Code53.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    GPT-5.6 Terra—
    Kimi K2.7 Code35.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Kimi K2.7 Code—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Kimi K2.7 Code—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Kimi K2.7 Code—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Kimi K2.7 Code—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Kimi K2.7 Code—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Kimi K2.7 Code?

GPT-5.6 Terra has the higher public score estimate, 72.29 versus 54.07, 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 Kimi K2.7 Code?

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 Kimi K2.7 Code?

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 Kimi K2.7 Code?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.00295 on Kimi K2.7 Code; repository review costs $0.136 and $0.0595; the cache-heavy agent loop costs $0.2 and $0.249. Kimi K2.7 Code 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 Kimi K2.7 Code?

GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

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Last updated August 10, 2026

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