Skip to main content
Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

Start free brief
BenchLM
Model A
Mistral Medium 3.5 128B

Mistral

—

Evidence status unavailable

90% interval unavailable

Mistral Medium 3.5 128B vs Ornith-1.0-9B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Ornith-1.0-9B

DeepReinforce AI

—

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
Mistral Medium 3.5 128B only
3
Ornith-1.0-9B only
6
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
Mistral Medium 3.5 128B
77.6
Ornith-1.0-9B
59.2
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
43.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Mistral Medium 3.5 128B
Not measured
Ornith-1.0-9B
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

Mistral Medium 3.5 128B
$0.00525
Fits in one request
Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-9B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Mistral Medium 3.5 128B
$0.0975
Fits in one request
Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-9B has no comparable published API token rate.

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
Ornith-1.0-9B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.0-9B has no comparable published API token 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

Ornith-1.0-9B

256K

API model ID

Mistral Medium 3.5 128B

Not sourced

Ornith-1.0-9B

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

Ornith-1.0-9B

No comparable hosted API rate

Documented inputs

Mistral Medium 3.5 128B

Not sourced

Ornith-1.0-9B

Not sourced

Documented outputs

Mistral Medium 3.5 128B

Not sourced

Ornith-1.0-9B

Not sourced

Provider availability

Mistral Medium 3.5 128B

Not sourced

Ornith-1.0-9B

Not sourced

Reasoning profile

Mistral Medium 3.5 128B

Reasoning

Ornith-1.0-9B

Reasoning

Weight access

Mistral Medium 3.5 128B

Open Weight

Ornith-1.0-9B

Open Weight

License

Mistral Medium 3.5 128B

Open Weight

Ornith-1.0-9B

Open Weight

Release date

Mistral Medium 3.5 128B

2026-04-29

Ornith-1.0-9B

2026-06-01

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 256K.

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 evidence10 rows

Agentic

  • τ³-bench results

    Mistral Medium 3.5 128B91.4%
    Source
    Ornith-1.0-9B—

    Not directly comparable

  • Gert Labs

    Mistral Medium 3.5 128B39.10%
    Source
    Ornith-1.0-9B—

    Not directly comparable

  • Terminal-Bench 2.0

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B43.1%
    Source

    Not directly comparable

  • Claw-Eval

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B63.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Mistral Medium 3.5 128B77.6%
    Source
    Ornith-1.0-9B69.4%
    Source

    Mistral Medium 3.5 128B leads this result

  • EEBench

    Mistral Medium 3.5 128B5.9%
    Source
    Ornith-1.0-9B—

    Not directly comparable

  • SWE-bench Pro

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B42.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B52%
    Source

    Not directly comparable

  • NL2Repo

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B27.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Mistral Medium 3.5 128B—
    Ornith-1.0-9B43.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Mistral Medium 3.5 128B or Ornith-1.0-9B?

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, Mistral Medium 3.5 128B or Ornith-1.0-9B?

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, Mistral Medium 3.5 128B or Ornith-1.0-9B?

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, Mistral Medium 3.5 128B or Ornith-1.0-9B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Mistral Medium 3.5 128B or Ornith-1.0-9B?

Both models list the same context window, 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

Watch Mistral Medium 3.5 128B vs Ornith-1.0-9B

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

Read a sample issue

Join 2,000+ readers.