Skip to content
ModelCap

Mistral Nemo

Official publisher releaseOpen weights

Mistral AI

Mistral Nemo is a language model from Mistral AI. It does not hold a position on ModelCap's current board yet; it is placed automatically once its model card or a public benchmark compares it with ranked models. It is listed at $0.029 per million input tokens and $0.03 per million output tokens across 5 providers. It reads up to 131K tokens of context, and its weights are openly downloadable.

Figures as of 6 Nov 2026, 20:32 UTC · download the public dataset · methodology

ModelCap Index · Current board
—
Index position pending
Market Gravity
43.9Increased by 0.2
Secondary market signal
Input
$0.029
per 1M tokens
Output
$0.03
per 1M tokens
Context
131K
tokens
Providers
5
endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
Io Netfp16Cheapest$0.017$0.022128Kfp16100.0%
DeepInfrafp8$0.029$0.03131Kfp8100.0%
DekaLLMfp8$0.018$0.03131Kfp8100.0%
Parasailfp8$0.03$0.03131Kfp8100.0%
Mistraleu$0.15$0.15131K—100.0%

Reported evaluation leads (7)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. They remain quarantined until dataset revision, harness, configuration and identity pass ModelCap’s independent admission review.

Dataset / taskMetricValueRevisionProvenance
thamilvendhan/signalbench
src
—0.3167
Unpinned
not published
Reported
Quarantined
thamilvendhan/signalbench
time
—0.5833
Unpinned
not published
Reported
Quarantined
thamilvendhan/signalbench
access_deny
—0.0833
Unpinned
not published
Reported
Quarantined
thamilvendhan/signalbench
memory_label
—0.3333
Unpinned
not published
Reported
Quarantined
thamilvendhan/signalbench
injection
—0.5
Unpinned
not published
Reported
Quarantined
thamilvendhan/signalbench
bot_policy
—0.0833
Unpinned
not published
Reported
Quarantined
TIGER-Lab/MMLU-Pro
mmlu_pro
—44.81
Unpinned
not published
Reported
Quarantined

ModelCap Index · Current board

Methodology
—
Index position pending
Technical details
Public rank basis
Index position pending (abstained)
Evidence label
Index position pending · placement uncalibrated
Index support
0.0% · not applicable
Identity binding
Exact catalogue product · mistralai/mistralai/mistral-nemo · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata mistralai/Mistral-Nemo-Instruct-2407@04d8a90549d23fc6bd7f642064003592df51e9b3 (not the evaluation revision)
Index observations used
0
Architecture fallback
abstained · 0% coverage · 0 anchors
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
32.2
Liquidity (providers × uptime)
77.1
Open reach (HF downloads)
45.0
Surface (context / tools / modalities)
45.2
Freshness
4.1
  • · OpenRouter popularity #52
  • · 5 live providers
  • · HF 30d downloads 103036
  • · Catalogue: context 131072, tools
  • · First seen 2024-07-19

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
17.8
3.6–31.9 uncertainty interval
Excluded from rank
Confidence
78%
Training support
221 models · 129 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
80.0
strong · 80% metadata coverage
Not capability
Artifact reproducibility
100.0
Access & legal clarity
100.0
Deployability
100.0
Evaluation provenance
0.0

Not yet published: admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
43.9
OpenRouter weekly popularity
#52 of 339

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
32.2

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
77.1

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
45.0

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
4.1

Time since first public availability, on a six-month half-life.

Specification

Model ID
mistralai/mistral-nemo
Context window
131K tokens
Declared output cap
16K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Text
Outputs
Text
Tokenizer
Mistral
Cached input tokens
Not offered
First seen on OpenRouter
19 Jul 2024
Knowledge cutoff
2024-04-30

Capabilities

  • Not supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
04d8a90549d2
Parameters
12.2B
Architecture
MistralForCausalLM
Model type
mistral
Hugging Face downloads (30d)
103K
Downloads (all time)
10.7M
Likes
2K
Repository updated
28 Jul 2025, 17:16 UTC

Mistral Nemo: common questions

How does Mistral Nemo rank among AI models?

Mistral Nemo has no public ModelCap Index position as of 6 November 2026: the snapshot lacks enough matched benchmark evidence, or the identity match is unresolved. Its catalogue facts (price, context, providers) are shown without a rank.

How much does Mistral Nemo cost per 1M tokens?

Mistral Nemo is listed at $0.029 per 1M input tokens and $0.03 per 1M output tokens as of 6 November 2026; the lowest output price among 5 providers is $0.022 on Io Net. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.073 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Mistral Nemo?

Mistral Nemo has a published context window of 131K tokens (131,072). The snapshot lists a 16,384 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Mistral Nemo open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is mistralai/Mistral-Nemo-Instruct-2407 on Hugging Face.

Which API providers serve Mistral Nemo?

5 providers serve Mistral Nemo through OpenRouter as of 6 November 2026: Io Net, DekaLLM, DeepInfra, Parasail and Mistral. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

When was Mistral Nemo released?

Mistral Nemo first appeared in the catalogue on 19 Jul 2024, with a published knowledge cutoff of 2024-04-30. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.