Skip to main content
Radar

Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.

See Radar
BenchLM
Advertise

Model comparison

GPT-5.6 Terra vs Mistral Large 3

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

Mistral Large 3

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 Terra

    GPT-5.6 Terra has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Mistral Large 3

    Mistral Large 3 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

    Mistral Large 3

    Mistral Large 3 has the lower estimated token cost for this stated workload. Mistral Large 3 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

    Mistral Large 3

    Mistral Large 3 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 Terra only
22
Mistral Large 3 only
0
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
Mistral Large 3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GPT-5.6 Terra
63.4
Mistral Large 3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.6 Terra
83.9
Mistral Large 3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.6 Terra
92.9
Mistral Large 3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.6 Terra
80.8
Mistral Large 3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.6 Terra
Not measured
Mistral Large 3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.6 Terra
80.7
Mistral Large 3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.6 Terra
Not measured
Mistral Large 3
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
Mistral Large 3
$0.00125
Fits in one request

Mistral Large 3 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
Mistral Large 3
$0.0295
Fits in one request

Mistral Large 3 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
Mistral Large 3
$0.125
Fits in one request
Cached input priced at the published list-input rate

Mistral Large 3 has the lower modeled cost

Mistral Large 3 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

Mistral Large 3

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

Mistral Large 3

Not published

Provider availability

GPT-5.6 Terra

Generally Available · OpenAI Responses API

OpenAI model catalog

Mistral Large 3

Not sourced

Reasoning profile

GPT-5.6 Terra

Reasoning

Mistral Large 3

Non-Reasoning

Weight access

GPT-5.6 Terra

Proprietary

Mistral Large 3

Proprietary

License

GPT-5.6 Terra

Proprietary

Mistral Large 3

Proprietary

Release date

GPT-5.6 Terra

2026-07-09

Mistral Large 3

2025-12-02

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: $0.136 vs $0.0295. Cache-heavy agent loop: $0.2 vs $0.125.
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.
Mistral Large 3
API / mo$1,500
Self-host / mo$9,110
Break-even380M/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 evidence22 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Mistral Large 3—

    Not directly comparable

  • BrowseComp

    GPT-5.6 Terra87.5%
    Source
    Mistral Large 3—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.6 Terra50.2%
    Source
    Mistral Large 3—

    Not directly comparable

  • CyberGym

    GPT-5.6 Terra81.8%
    Source
    Mistral Large 3—

    Not directly comparable

  • ExploitGym

    GPT-5.6 Terra23.2%
    Source
    Mistral Large 3—

    Not directly comparable

  • Toolathlon

    GPT-5.6 Terra53.1%
    Source
    Mistral Large 3—

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.6 Terra63.4%
    Source
    Mistral Large 3—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.6 Terra87.4%
    Source
    Mistral Large 3—

    Not directly comparable

  • deepSwe

    GPT-5.6 Terra69.6%
    Source
    Mistral Large 3—

    Not directly comparable

  • FrontierCode 1.1 Extended

    GPT-5.6 Terra55.8%
    Source
    Mistral Large 3—

    Not directly comparable

  • cursorBench32

    GPT-5.6 Terra64.9%
    Source
    Mistral Large 3—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.6 Terra83.9%
    Source
    Mistral Large 3—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.6 Terra0.8%
    Source
    Mistral Large 3—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.6 Terra92.9%
    Source
    Mistral Large 3—

    Not directly comparable

  • GPQA-D

    GPT-5.6 Terra92.9%
    Source
    Mistral Large 3—

    Not directly comparable

  • HealthBench Professional

    GPT-5.6 Terra57.7%
    Source
    Mistral Large 3—

    Not directly comparable

  • HealthBench Hard

    GPT-5.6 Terra32.7%
    Source
    Mistral Large 3—

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.6 Terra84.9%
    Source
    Mistral Large 3—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.6 Terra84.900%
    Source
    Mistral Large 3—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.6 Terra68.300%
    Source
    Mistral Large 3—

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.6 Terra80.7%
    Source
    Mistral Large 3—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.6 Terra82%
    Source
    Mistral Large 3—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.6 Terra or Mistral Large 3?

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 Terra or Mistral Large 3?

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 Mistral Large 3?

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 Mistral Large 3?

For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.00125 on Mistral Large 3; repository review costs $0.136 and $0.0295; the cache-heavy agent loop costs $0.2 and $0.125. Mistral Large 3 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 Mistral Large 3?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

Watch GPT-5.6 Terra vs Mistral Large 3

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.