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Thinking Machines Lab logo
Model A
Inkling

Thinking Machines Lab

67.02/100

Supported · Public rank #31

90% interval 60.0–74.0

Inkling vs Mistral Medium 3.5 128B

Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Mistral logo
Model B
Mistral Medium 3.5 128B

Mistral

Evidence status unavailable

90% interval unavailable

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality 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

    Inkling

    Inkling has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Inkling

    Inkling 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

    Inkling

    Inkling 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

    Mistral Medium 3.5 128B

    Mistral Medium 3.5 128B 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

  • 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
Inkling only
14
Mistral Medium 3.5 128B only
2
Like-for-like categories
0 / 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.

Coding

Directional only
Inkling
68.6
Mistral Medium 3.5 128B
77.6
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Inkling
69.4
Mistral Medium 3.5 128B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Inkling
Not measured
Mistral Medium 3.5 128B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Inkling
51.6
Mistral Medium 3.5 128B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Inkling
97.1
Mistral Medium 3.5 128B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Inkling
Not measured
Mistral Medium 3.5 128B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Inkling
76.5
Mistral Medium 3.5 128B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Inkling
79.8
Mistral Medium 3.5 128B
Not measured
Weighted basis
1 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

Inkling
$0.00421
Fits in one request
Mistral Medium 3.5 128B
$0.00525
Fits in one request

Inkling has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Inkling
$0.10754
Fits in one request
Mistral Medium 3.5 128B
$0.0975
Fits in one request

Mistral Medium 3.5 128B has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Inkling
$0.159
Fits in one request
Mistral Medium 3.5 128B
$0.405
Fits in one request
Cached input priced at the published list-input rate

Inkling 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.

Inkling

1M

Mistral Medium 3.5 128B

256K

API model ID

Inkling

Not sourced

Mistral Medium 3.5 128B

Not sourced

Cached-input rate

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

Inkling

$0.374 per 1M cached input tokens

Mistral Medium 3.5 128B

Not published

Documented inputs

Inkling

Not sourced

Mistral Medium 3.5 128B

Not sourced

Documented outputs

Inkling

Not sourced

Mistral Medium 3.5 128B

Not sourced

Provider availability

Inkling

Not sourced

Mistral Medium 3.5 128B

Not sourced

Reasoning profile

Inkling

Hybrid

Mistral Medium 3.5 128B

Reasoning

Weight access

Inkling

Open Weight

Mistral Medium 3.5 128B

Open Weight

License

Inkling

Open Weight

Mistral Medium 3.5 128B

Open Weight

Release date

Inkling

2026-07-15

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
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: $0.10754 vs $0.0975. Cache-heavy agent loop: $0.159 vs $0.405.
Context tradeoff
Inkling has the larger documented window (1M).

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 evidence17 rows

Agentic

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • BrowseComp

    Inkling77.1%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • MCP Atlas

    Inkling74.1%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • τ³-bench results

    Inkling
    Mistral Medium 3.5 128B91.4%
    Source

    Not directly comparable

  • Gert Labs

    Inkling
    Mistral Medium 3.5 128B39.10%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Inkling77.6%
    Source
    Mistral Medium 3.5 128B77.6%
    Source

    Tie

  • SWE-bench Pro

    Inkling54.3%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

Knowledge

  • GPQA

    Inkling87.9%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • GPQA-D

    Inkling87.9%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • HLE

    Inkling46%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • HLE w/o tools

    Inkling30%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

Math

  • AIME26

    Inkling97.1%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

Multimodal

  • MMMU-Pro

    Inkling73.5%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • CharXiv

    Inkling82%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

  • CharXiv w/o tools

    Inkling78.1%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

Instruction following

  • IFBench

    Inkling79.8%
    Source
    Mistral Medium 3.5 128B

    Not directly comparable

Frequently asked questions

Which is better, Inkling or Mistral Medium 3.5 128B?

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, Inkling or Mistral Medium 3.5 128B?

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, Inkling 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, Inkling or Mistral Medium 3.5 128B?

For the stated presets, chat costs $0.00421 on Inkling and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.10754 and $0.0975; the cache-heavy agent loop costs $0.159 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, Inkling or Mistral Medium 3.5 128B?

Inkling has the larger documented context window: 1M, compared with 256K.

Related comparisons

Last updated August 29, 2026

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