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Mistral logo
Model A
Mistral Medium 3.5 128B

Mistral

48.95/100

Estimated · Public rank #147

90% interval 37.460.5

Mistral Medium 3.5 128B vs Fugu Cyber

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Sakana AI logo
Model B
Fugu Cyber

Sakana AI

Evidence status unavailable

90% interval unavailable

Decision reading

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Fugu Cyber

    Fugu Cyber has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 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

  • Cache-heavy agent loop cost

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

    Mistral Medium 3.5 128B

    Mistral Medium 3.5 128B 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

    Fugu Cyber is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Fugu Cyber is not ranked on the public lane for agentic, so no winner is named for agentic.

    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
Mistral Medium 3.5 128B only
7
Fugu Cyber only
2
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
Mistral Medium 3.5 128B
27.5
Supported · #141/151
Fugu Cyber
Not ranked
Basis
BenchAlign lane · 3 vs 2 public rows
Reading
Not comparable

Coding

Not comparable
Mistral Medium 3.5 128B
41.1
Estimated · #136/183
Fugu Cyber
Not ranked
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Mistral Medium 3.5 128B
68.0
Unranked · 2 rankable rows
Fugu Cyber
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Mistral Medium 3.5 128B
42.0
Supported · #131/181
Fugu Cyber
Not ranked
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Not comparable

Math

Not comparable
Mistral Medium 3.5 128B
Not ranked
Fugu Cyber
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Mistral Medium 3.5 128B
Not ranked
Fugu Cyber
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Mistral Medium 3.5 128B
55.4
Unranked · 1 rankable row
Fugu Cyber
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Mistral Medium 3.5 128B
83.7
#47/120
Fugu Cyber
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Mistral Medium 3.5 128B
$0.00525
Fits in one request
Fugu Cyber
$0.024
Fits in one request

Mistral Medium 3.5 128B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Mistral Medium 3.5 128B
$0.0975
Fits in one request
Fugu Cyber
$0.408
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

Mistral Medium 3.5 128B
$0.405
Fits in one request
Cached input priced at the published list-input rate
Fugu Cyber
$0.6
Fits in one request

Mistral Medium 3.5 128B 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.

Mistral Medium 3.5 128B

256K

Fugu Cyber

1M

API model ID

Mistral Medium 3.5 128B

Not sourced

Fugu Cyber

Not sourced

Cached-input rate

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

Mistral Medium 3.5 128B

Not published

Fugu Cyber

$0.6 per 1M cached input tokens

Documented inputs

Mistral Medium 3.5 128B

Not sourced

Fugu Cyber

Not sourced

Documented outputs

Mistral Medium 3.5 128B

Not sourced

Fugu Cyber

Not sourced

Provider availability

Mistral Medium 3.5 128B

Not sourced

Fugu Cyber

Not sourced

Reasoning profile

Mistral Medium 3.5 128B

Reasoning

Fugu Cyber

Reasoning

Weight access

Mistral Medium 3.5 128B

Open Weight

Fugu Cyber

Proprietary

License

Mistral Medium 3.5 128B

Open Weight

Fugu Cyber

Proprietary

Release date

Mistral Medium 3.5 128B

2026-04-29

Fugu Cyber

2026-07-21

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: $0.0975 vs $0.408. Cache-heavy agent loop: $0.405 vs $0.6.
Context tradeoff
Fugu Cyber 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 evidence9 rows

Agentic

  • τ³-bench results

    Mistral Medium 3.5 128B91.4%
    Source
    Fugu Cyber

    Not directly comparable

  • Gert Labs

    Mistral Medium 3.5 128B39.10%
    Source
    Fugu Cyber

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Mistral Medium 3.5 128B39.0%
    Source
    Fugu Cyber

    Not directly comparable

  • CyberGym

    Mistral Medium 3.5 128B
    Fugu Cyber86.9%
    Source

    Not directly comparable

  • CTI-REALM

    Mistral Medium 3.5 128B
    Fugu Cyber72.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Mistral Medium 3.5 128B77.6%
    Source
    Fugu Cyber

    Not directly comparable

  • SWE-bench (Vals)

    Mistral Medium 3.5 128B66.4%
    Source
    Fugu Cyber

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Mistral Medium 3.5 128B34.8%
    Source
    Fugu Cyber

    Not directly comparable

  • MMLU-Pro (Vals)

    Mistral Medium 3.5 128B75.3%
    Source
    Fugu Cyber

    Not directly comparable

Frequently asked questions

Which is better, Mistral Medium 3.5 128B or Fugu Cyber?

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, Mistral Medium 3.5 128B or Fugu Cyber?

Fugu Cyber is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Mistral Medium 3.5 128B or Fugu Cyber?

Fugu Cyber is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Mistral Medium 3.5 128B or Fugu Cyber?

For the stated presets, chat costs $0.00525 on Mistral Medium 3.5 128B and $0.024 on Fugu Cyber; repository review costs $0.0975 and $0.408; the cache-heavy agent loop costs $0.405 and $0.6. 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, Mistral Medium 3.5 128B or Fugu Cyber?

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

Related comparisons

Last updated September 4, 2026

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