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Model comparison

GPT-5.6 Luna vs Mistral Medium 3.5 128B

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

GPT-5.6 Luna

OpenAI

66.9/100

Estimated · Public rank #23

90% interval 56.4–77.3

Mistral Medium 3.5 128B

Mistral

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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. Mistral Medium 3.5 128B 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

    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
0
GPT-5.6 Luna only
22
Mistral Medium 3.5 128B only
3
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 Luna
84.1
Mistral Medium 3.5 128B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Luna
62.7
Mistral Medium 3.5 128B
77.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Luna
59.5
Mistral Medium 3.5 128B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Luna
92.3
Mistral Medium 3.5 128B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Luna
73.6
Mistral Medium 3.5 128B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Luna
Not measured
Mistral Medium 3.5 128B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Luna
78.4
Mistral Medium 3.5 128B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Luna
Not measured
Mistral Medium 3.5 128B
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 Luna
$0.0008
Fits in one request
Mistral Medium 3.5 128B
$0.00525
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
Mistral Medium 3.5 128B
$0.0975
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
Mistral Medium 3.5 128B
$0.405
Fits in one request
Cached input priced at the published list-input rate

GPT-5.6 Luna has the lower modeled cost

Mistral Medium 3.5 128B 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 Luna

1.05M

OpenAI model catalog

Mistral Medium 3.5 128B

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 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Mistral Medium 3.5 128B

Not published

Provider availability

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Mistral Medium 3.5 128B

Not sourced

Reasoning profile

GPT-5.6 Luna

Reasoning

Mistral Medium 3.5 128B

Reasoning

Weight access

GPT-5.6 Luna

Proprietary

Mistral Medium 3.5 128B

Open Weight

License

GPT-5.6 Luna

Proprietary

Mistral Medium 3.5 128B

Open Weight

Release date

GPT-5.6 Luna

2026-07-09

Mistral Medium 3.5 128B

2026-04-29

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0136 vs $0.0975. Cache-heavy agent loop: $0.02 vs $0.405.
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 evidence25 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Luna84.7%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Luna83.3%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Luna45.6%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • CyberGym

    GPT-5.6 Luna77.9%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Luna12.4%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Luna53.4%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • τ³-bench results

    GPT-5.6 Luna—
    Mistral Medium 3.5 128B91.4%
    Source

    Not directly comparable

  • Gert Labs

    GPT-5.6 Luna—
    Mistral Medium 3.5 128B39.10%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Luna62.7%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Luna84.7%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • deepSwe

    GPT-5.6 Luna67.2%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Luna55.1%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Luna61.1%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • SWE-bench Verified

    GPT-5.6 Luna—
    Mistral Medium 3.5 128B77.6%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Luna59.5%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Luna0.2%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Luna92.3%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Luna92.3%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Luna55.7%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Luna32.0%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Luna78.6%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Luna78.600%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Luna58.500%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Luna78.4%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Luna79.5%
    Source
    Mistral Medium 3.5 128B—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Luna or Mistral Medium 3.5 128B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.6 Luna or Mistral Medium 3.5 128B?

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 Luna or Mistral Medium 3.5 128B?

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 Luna or Mistral Medium 3.5 128B?

For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0136 and $0.0975; the cache-heavy agent loop costs $0.02 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.6 Luna or Mistral Medium 3.5 128B?

GPT-5.6 Luna 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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