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BenchLM
Data

Mistral Medium 3.5 128B vs Nemotron 3 Ultra

Updated October 14, 2026. Rank says Nemotron 3 Ultra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Nemotron 3 Ultra has the higher public point estimate, 47.25 versus 33.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Mistral logo

Mistral

33.8/100

Estimated · Public rank #160

Conditional range 19.4–48.1

Model B
NVIDIA logo

NVIDIA

47.25/100

Estimated · Public rank #105

Conditional range 32.9–61.6

Shared results
6
Mistral Medium 3.5 128B only
1
Nemotron 3 Ultra only
18
Like-for-like categories
1 / 8
Estimated: Mistral Medium 3.5 128B and Nemotron 3 Ultra. Conditional ranges do not establish rank confidence.How the comparison works

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.

  • Agentic work

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

    Nemotron 3 Ultra

    Nemotron 3 Ultra has the higher public agentic point estimate, 28.2 to 20.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Nemotron 3 Ultra

    Nemotron 3 Ultra has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

22.7Mistral Medium 3.5 128B21.9Nemotron 3 Ultra

Directional only · BenchAlign v5.8

Mistral Medium 3.5 128B has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Like-for-like
Mistral Medium 3.5 128B
20.1
Supported · #105/125
Nemotron 3 Ultra
28.2
Supported · #95/125
Basis
BenchAlign v5.8 lane · 3 vs 6 public rows
Reading
Nemotron 3 Ultra leads · intervals overlap

Coding

Directional only
Mistral Medium 3.5 128B
22.7
Estimated · #113/149
Nemotron 3 Ultra
21.9
Supported · #115/149
Basis
BenchAlign v5.8 lane · 2 vs 7 public rows
Reading
Directional only

Knowledge

Directional only
Mistral Medium 3.5 128B
31.8
Supported · #145/181
Nemotron 3 Ultra
50.4
Estimated · #77/181
Basis
BenchAlign v5.8 lane · 2 vs 7 public rows
Reading
Directional only

Instruction following

Directional only
Mistral Medium 3.5 128B
84.0
#50/144
Nemotron 3 Ultra
92.1
#20/144
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
Mistral Medium 3.5 128B
73.5
Unranked · 2 rankable rows
Nemotron 3 Ultra
70.8
#20/29
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Mistral Medium 3.5 128B
60.1
Unranked · 1 rankable row
Nemotron 3 Ultra
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Mistral Medium 3.5 128B
Not ranked
Nemotron 3 Ultra
89.2
#7/17
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Mistral Medium 3.5 128B
Not ranked
Nemotron 3 Ultra
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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
Nemotron 3 Ultra
Self-hosted; infrastructure cost varies
Fits in one request

Nemotron 3 Ultra 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
Nemotron 3 Ultra
Self-hosted; infrastructure cost varies
Fits in one request

Nemotron 3 Ultra 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
Nemotron 3 Ultra
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. Nemotron 3 Ultra has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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

Nemotron 3 Ultra

1M

API model ID

Mistral Medium 3.5 128B

Not sourced

Nemotron 3 Ultra

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

Nemotron 3 Ultra

No comparable hosted API rate

Documented inputs

Mistral Medium 3.5 128B

Not sourced

Nemotron 3 Ultra

Not sourced

Documented outputs

Mistral Medium 3.5 128B

Not sourced

Nemotron 3 Ultra

Not sourced

Provider availability

Mistral Medium 3.5 128B

Not sourced

Nemotron 3 Ultra

Not sourced

Reasoning profile

Mistral Medium 3.5 128B

Reasoning

Nemotron 3 Ultra

Reasoning

Weight access

Mistral Medium 3.5 128B

Open Weight

Nemotron 3 Ultra

Open Weight

License

Mistral Medium 3.5 128B

Open Weight

Nemotron 3 Ultra

Open Weight

Release date

Mistral Medium 3.5 128B

2026-04-29

Nemotron 3 Ultra

2026-06-04

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
Nemotron 3 Ultra has the higher public point estimate, 47.25 versus 33.8. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Nemotron 3 Ultra has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Mistral Medium 3.5 128B or Nemotron 3 Ultra?

Nemotron 3 Ultra has the higher public point estimate, 47.25 versus 33.8. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Mistral Medium 3.5 128B or Nemotron 3 Ultra?

Mistral Medium 3.5 128B scores higher for coding on the public lane, 22.7 to 21.9. Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; 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 Nemotron 3 Ultra?

Nemotron 3 Ultra has the higher public agentic tasks point estimate, 28.2 to 20.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Mistral Medium 3.5 128B or Nemotron 3 Ultra?

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 Nemotron 3 Ultra?

Nemotron 3 Ultra has the larger documented context window: 1M, compared with 256K.

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

  • τ³-bench results

    Mistral Medium 3.5 128B91.4%
    Source
    Nemotron 3 Ultra70.9%
    Source

    Mistral Medium 3.5 128B leads this result

  • Gert Labs

    Mistral Medium 3.5 128B39.10%
    Source
    Nemotron 3 Ultra—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Mistral Medium 3.5 128B39.0%
    Source
    Nemotron 3 Ultra50.9%
    Source

    Nemotron 3 Ultra leads this result

  • Terminal-Bench 2.1

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra56.4%
    Source

    Not directly comparable

  • PinchBench

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra90.0%
    Source

    Not directly comparable

  • BrowseComp

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra44.4%
    Source

    Not directly comparable

  • HLE w/ tools

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra37.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Mistral Medium 3.5 128B77.6%
    Source
    Nemotron 3 Ultra71.9%
    Source

    Mistral Medium 3.5 128B leads this result

  • SWE-bench (Vals)

    Mistral Medium 3.5 128B66.4%
    Source
    Nemotron 3 Ultra69.0%
    Source

    Nemotron 3 Ultra leads this result

  • SWE Multilingual

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra67.7%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra89.0%
    Source

    Not directly comparable

  • SciCode

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra44.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra56.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra86.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra3.1%
    Source

    Not directly comparable

  • LongBench v2

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra61.9%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Mistral Medium 3.5 128B34.8%
    Source
    Nemotron 3 Ultra86.1%
    Source

    Nemotron 3 Ultra leads this result

  • MMLU-Pro (Vals)

    Mistral Medium 3.5 128B75.3%
    Source
    Nemotron 3 Ultra85.8%
    Source

    Nemotron 3 Ultra leads this result

  • GPQA

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra87%
    Source

    Not directly comparable

  • GPQA-D

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra87.0%
    Source

    Not directly comparable

  • HLE

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra26.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra26.7%
    Source

    Not directly comparable

  • MMLU-Pro

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra86.8%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra83%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Mistral Medium 3.5 128B—
    Nemotron 3 Ultra81.7%
    Source

    Not directly comparable

25 public results · 6 shared

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Last updated October 14, 2026