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Model comparison

GPT-5.6 Luna vs Inkling-Small

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GPT-5.6 Luna

OpenAI

66.6/100

Estimated · Public rank #25

90% interval 56.1–77.0

Inkling-Small

Thinking Machines Lab

—

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.

8 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.

  • Agentic work

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

    GPT-5.6 Luna

    GPT-5.6 Luna leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Luna

    GPT-5.6 Luna has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.6 Luna

    GPT-5.6 Luna 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

  • Cache-heavy agent loop cost

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

    GPT-5.6 Luna

    GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.6 Luna

    GPT-5.6 Luna 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

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
8
GPT-5.6 Luna only
14
Inkling-Small only
12
Like-for-like categories
2 / 8

3 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

Like-for-like
GPT-5.6 Luna
84.1
Inkling-Small
70.1
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Luna leads

Reasoning

Like-for-like
GPT-5.6 Luna
59.5
Inkling-Small
40.1
Weighted basis
1 vs 1 rows
Reading
GPT-5.6 Luna leads

Coding

Directional only
GPT-5.6 Luna
62.7
Inkling-Small
62.4
Weighted basis
1 vs 3 rows
Reading
Directional only

Knowledge

Directional only
GPT-5.6 Luna
92.3
Inkling-Small
53.4
Weighted basis
1 vs 2 rows
Reading
Directional only

Multimodal

Directional only
GPT-5.6 Luna
78.4
Inkling-Small
76.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Math

Not comparable
GPT-5.6 Luna
73.6
Inkling-Small
92.9
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Luna
Not measured
Inkling-Small
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Luna
Not measured
Inkling-Small
82.2
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 Luna
$0.0008
Fits in one request
Inkling-Small
$0.0013
Fits in one request

GPT-5.6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.6 Luna
$0.0136
Fits in one request
Inkling-Small
$0.03332
Fits in one request

GPT-5.6 Luna 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 Luna
$0.02
Fits in one request
Inkling-Small
$0.0492
Fits in one request

GPT-5.6 Luna 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.

Context window

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

GPT-5.6 Luna

1.05M

OpenAI model catalog

Inkling-Small

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 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Inkling-Small

$0.116 per 1M cached input tokens

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Inkling-Small

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Inkling-Small

Hybrid

Weight access

GPT-5.6 Luna

Proprietary

Inkling-Small

Open Weight

License

GPT-5.6 Luna

Proprietary

Inkling-Small

Open Weight

Release date

GPT-5.6 Luna

2026-07-09

Inkling-Small

2026-07-30

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
Repository review: $0.0136 vs $0.03332. Cache-heavy agent loop: $0.02 vs $0.0492.
Context tradeoff
GPT-5.6 Luna 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 evidence34 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Luna84.7%
    Source
    Inkling-Small64.7%
    Source

    GPT-5.6 Luna leads this result

  • BrowseComp

    GPT-5.6 Luna83.3%
    Source
    Inkling-Small77.4%
    Source

    GPT-5.6 Luna leads this result

  • OSWorld 2.0

    GPT-5.6 Luna45.6%
    Source
    Inkling-Small—

    Not directly comparable

  • CyberGym

    GPT-5.6 Luna77.9%
    Source
    Inkling-Small—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Luna12.4%
    Source
    Inkling-Small—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Luna53.4%
    Source
    Inkling-Small—

    Not directly comparable

  • MCP Atlas

    GPT-5.6 Luna—
    Inkling-Small79.6%
    Source

    Not directly comparable

  • Toolathlon-Verified

    GPT-5.6 Luna—
    Inkling-Small54.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Luna62.7%
    Source
    Inkling-Small55.9%
    Source

    GPT-5.6 Luna leads this result

  • Terminal-Bench 2.0

    GPT-5.6 Luna84.7%
    Source
    Inkling-Small64.7%
    Source

    GPT-5.6 Luna leads this result

  • deepSwe

    GPT-5.6 Luna67.2%
    Source
    Inkling-Small—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Luna55.1%
    Source
    Inkling-Small—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Luna61.1%
    Source
    Inkling-Small—

    Not directly comparable

  • SWE-bench Verified

    GPT-5.6 Luna—
    Inkling-Small80.2%
    Source

    Not directly comparable

  • SciCode

    GPT-5.6 Luna—
    Inkling-Small48.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Luna59.5%
    Source
    Inkling-Small40.1%
    Source

    GPT-5.6 Luna leads this result

  • ARC-AGI-3

    GPT-5.6 Luna0.2%
    Source
    Inkling-Small—

    Not directly comparable

  • CritPt

    GPT-5.6 Luna—
    Inkling-Small8.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Luna92.3%
    Source
    Inkling-Small89.5%
    Source

    GPT-5.6 Luna leads this result

  • GPQA-D

    GPT-5.6 Luna92.3%
    Source
    Inkling-Small89.5%
    Source

    GPT-5.6 Luna leads this result

  • HealthBench Professional

    GPT-5.6 Luna55.7%
    Source
    Inkling-Small—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Luna32.0%
    Source
    Inkling-Small—

    Not directly comparable

  • HLE

    GPT-5.6 Luna—
    Inkling-Small47.8%
    Source

    Not directly comparable

  • HLE w/o tools

    GPT-5.6 Luna—
    Inkling-Small31.6%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Luna78.6%
    Source
    Inkling-Small—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Luna78.600%
    Source
    Inkling-Small—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Luna58.500%
    Source
    Inkling-Small—

    Not directly comparable

  • AIME26

    GPT-5.6 Luna—
    Inkling-Small95.5%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-5.6 Luna—
    Inkling-Small90.2%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Luna78.4%
    Source
    Inkling-Small74%
    Source

    GPT-5.6 Luna leads this result

  • MMMU-Pro w/ Python

    GPT-5.6 Luna79.5%
    Source
    Inkling-Small—

    Not directly comparable

  • CharXiv

    GPT-5.6 Luna—
    Inkling-Small81.3%
    Source

    Not directly comparable

  • CharXiv w/o tools

    GPT-5.6 Luna—
    Inkling-Small77.4%
    Source

    Not directly comparable

Instruction following

  • IFBench

    GPT-5.6 Luna—
    Inkling-Small82.2%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Luna or Inkling-Small?

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 Luna or Inkling-Small?

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, GPT-5.6 Luna or Inkling-Small?

GPT-5.6 Luna leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.

Which costs less, GPT-5.6 Luna or Inkling-Small?

For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.0013 on Inkling-Small; repository review costs $0.0136 and $0.03332; the cache-heavy agent loop costs $0.02 and $0.0492. Costs use the listed standard API rates.

Which has the larger context window, GPT-5.6 Luna or Inkling-Small?

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

Related comparisons

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Last updated July 30, 2026

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