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Model comparison

Gemini 2.5 Pro vs GPT-5.6 Luna

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

Gemini 2.5 Pro

Google

56.6/100

Supported · Public rank #79

90% interval 39.3–73.9

GPT-5.6 Luna

OpenAI

66.9/100

Estimated · Public rank #23

90% interval 56.4–77.3

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 56.58, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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 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

  • 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

Show secondary and unsupported calls

  • 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

    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
3
Gemini 2.5 Pro only
4
GPT-5.6 Luna only
19
Like-for-like categories
1 / 8

1 category uses 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.

Math

Like-for-like
Gemini 2.5 Pro
11.6
GPT-5.6 Luna
73.6
Weighted basis
2 vs 2 rows
Reading
GPT-5.6 Luna leads

Knowledge

Directional only
Gemini 2.5 Pro
27.4
GPT-5.6 Luna
92.3
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Gemini 2.5 Pro
Not measured
GPT-5.6 Luna
84.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Gemini 2.5 Pro
63.8
GPT-5.6 Luna
62.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 2.5 Pro
Not measured
GPT-5.6 Luna
59.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 2.5 Pro
Not measured
GPT-5.6 Luna
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 2.5 Pro
Not measured
GPT-5.6 Luna
78.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 2.5 Pro
Not measured
GPT-5.6 Luna
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

Gemini 2.5 Pro
$0.00625
Fits in one request
GPT-5.6 Luna
$0.0008
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

Gemini 2.5 Pro
$0.0925
Fits in one request
GPT-5.6 Luna
$0.0136
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

Gemini 2.5 Pro
$0.15
Fits in one request
GPT-5.6 Luna
$0.02
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.

Cached-input rate

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

Gemini 2.5 Pro

$0.125 per 1M cached input tokens

Google Gemini API pricing

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

Gemini 2.5 Pro

Not sourced

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemini 2.5 Pro

Non-Reasoning

GPT-5.6 Luna

Reasoning

Weight access

Gemini 2.5 Pro

Proprietary

GPT-5.6 Luna

Proprietary

License

Gemini 2.5 Pro

Proprietary

GPT-5.6 Luna

Proprietary

Release date

Gemini 2.5 Pro

2025-03-01

GPT-5.6 Luna

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
GPT-5.6 Luna has the higher public score estimate, 66.87 versus 56.58, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0925 vs $0.0136. Cache-heavy agent loop: $0.15 vs $0.02.
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 evidence26 rows

Agentic

  • Gert Labs

    Gemini 2.5 Pro42.01%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 2.5 Pro—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • BrowseComp

    Gemini 2.5 Pro—
    GPT-5.6 Luna83.3%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini 2.5 Pro—
    GPT-5.6 Luna45.6%
    Source

    Not directly comparable

  • CyberGym

    Gemini 2.5 Pro—
    GPT-5.6 Luna77.9%
    Source

    Not directly comparable

  • ExploitGym

    Gemini 2.5 Pro—
    GPT-5.6 Luna12.4%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 2.5 Pro—
    GPT-5.6 Luna53.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Gemini 2.5 Pro63.8%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • Vibe Code Bench

    Gemini 2.5 Pro0.40%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • SWE-bench Pro

    Gemini 2.5 Pro—
    GPT-5.6 Luna62.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 2.5 Pro—
    GPT-5.6 Luna84.7%
    Source

    Not directly comparable

  • deepSwe

    Gemini 2.5 Pro—
    GPT-5.6 Luna67.2%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    Gemini 2.5 Pro—
    GPT-5.6 Luna55.1%
    Source

    Not directly comparable

  • cursorBench32

    Gemini 2.5 Pro—
    GPT-5.6 Luna61.1%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 2.5 Pro—
    GPT-5.6 Luna59.5%
    Source

    Not directly comparable

  • ARC-AGI-3

    Gemini 2.5 Pro—
    GPT-5.6 Luna0.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 2.5 Pro83%
    Source
    GPT-5.6 Luna92.3%
    Source

    GPT-5.6 Luna leads this result

  • HLE

    Gemini 2.5 Pro18.8%
    Source
    GPT-5.6 Luna—

    Not directly comparable

  • GPQA-D

    Gemini 2.5 Pro—
    GPT-5.6 Luna92.3%
    Source

    Not directly comparable

  • HealthBench Professional

    Gemini 2.5 Pro—
    GPT-5.6 Luna55.7%
    Source

    Not directly comparable

  • HealthBench Hard

    Gemini 2.5 Pro—
    GPT-5.6 Luna32.0%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 2.5 Pro14.138%
    Source
    GPT-5.6 Luna78.600%
    Source

    GPT-5.6 Luna leads this result

  • FrontierMath v2 (Tier 4)

    Gemini 2.5 Pro4.167%
    Source
    GPT-5.6 Luna58.500%
    Source

    GPT-5.6 Luna leads this result

  • FrontierMath (legacy)

    Gemini 2.5 Pro—
    GPT-5.6 Luna78.6%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 2.5 Pro—
    GPT-5.6 Luna78.4%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Gemini 2.5 Pro—
    GPT-5.6 Luna79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 2.5 Pro or GPT-5.6 Luna?

GPT-5.6 Luna has the higher public score estimate, 66.87 versus 56.58, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 2.5 Pro or GPT-5.6 Luna?

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, Gemini 2.5 Pro or GPT-5.6 Luna?

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, Gemini 2.5 Pro or GPT-5.6 Luna?

For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0008 on GPT-5.6 Luna; repository review costs $0.0925 and $0.0136; the cache-heavy agent loop costs $0.15 and $0.02. Costs use the listed standard API rates.

Which has the larger context window, Gemini 2.5 Pro or GPT-5.6 Luna?

GPT-5.6 Luna 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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