Mistral Nemo model profile

Evidence snapshot: 2026-09-06

Mistral Nemo is a Mistral AI model profile with a 131,072-token context window. The dated catalog snapshot records text modalities and interfaces for structured output, text generation, tool calling.

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi. It supports function calling and is released under the Apache 2.0 license.

Mistral Nemo is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Mistral Nemo is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.

Mistral Nemo accepts text and returns text. The snapshot records structured output, text generation, and tool calling as capabilities or interfaces.

The dated reference snapshot lists $0.019 input and $0.03 output per million tokens. Cached input is $0.029 per million tokens.

No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.

Mistral Nemo ranked #65 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

Profile facts

Profile type: Model research profile
Provider or publisher: Mistral AI
Context window: 131,072 tokens
Snapshot date: 2026-09-06
Knowledge cutoff: 2024-04-30
Reference catalog ID: mistralai/mistral-nemo

When to pick Mistral Nemo

Mistral Nemo is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

Reach for it when cost is the binding constraint. At $0.019 in and $0.03 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot.
Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
Look elsewhere when the prompt is long. The 131K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.

Context, modalities, and identity

Mistral Nemo accepts text and returns text. The snapshot records structured output, text generation, and tool calling as capabilities or interfaces.

Provider or publisher: Mistral AI
Context window: 131,072 tokens
Maximum output: 16,384 tokens
Input modalities: text
Output modalities: text
Knowledge cutoff: 2024-04-30

Model overview

Reference model ID: mistralai/mistral-nemo
Canonical version: mistralai/mistral-nemo
Hugging Face ID: mistralai/Mistral-Nemo-Instruct-2407

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters: frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

structured output
text generation
tool calling

Dated pricing snapshot

The dated reference snapshot lists $0.019 input and $0.03 output per million tokens. Cached input is $0.029 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

Price date: 2026-09-06
Input: $0.019 per 1M tokens
Cached input: $0.029 per 1M tokens
Output: $0.03 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 4 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints: 3
Best p50 latency: 0.44 s
Best p50 throughput: 27 tok/s
Availability with routing: 99.98% over the sampled window
Availability without routing: 96.38% over the sampled window
Performance date: 2026-09-06

Provider	Quantization	Input / 1M	Output / 1M	Cache read / 1M	Context	p50 latency	p50 throughput	Uptime (1d)
DeepInfra	fp8	$0.019 / 1M	$0.03 / 1M	No verified rate	131,072 tokens	0.55 s	21 tok/s	99.98%
Parasail	fp8	$0.03 / 1M	$0.03 / 1M	No verified rate	131,072 tokens	0.62 s	27 tok/s	99.79%
io.net	fp16	$0.044 / 1M	$0.16 / 1M	$0.029 / 1M	128,000 tokens	0.44 s	27 tok/s	99.82%
NovitaAI	fp8	$0.04 / 1M	$0.17 / 1M	No verified rate	60,288 tokens	5.53 s	4 tok/s	52.38%

Benchmark evidence and limitations

No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Popularity and market context

Third-party catalog rank: #65
Global market share: Not inferred
Observation date: 2026-09-06

Frequently asked questions

When should I use Mistral Nemo?

Mistral Nemo is a reference profile: Mistral AI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when cost is the binding constraint: At $0.019 in and $0.03 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the prompt is long: The 131K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.

What is Mistral Nemo?

Mistral Nemo is a Mistral AI model profile with a 131,072-token context window. The dated catalog snapshot records text modalities and interfaces for structured output, text generation, tool calling.

What context window does Mistral Nemo have?

The dated profile lists 131,072 tokens of context and up to 16,384 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

What benchmark evidence is available for Mistral Nemo?

No comparable third-party benchmark value is present in the 2026-09-06 snapshot. Missing values are not estimated. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Is Mistral Nemo available through Kendr?

This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.

Does the popularity rank represent Mistral Nemo's global market share?

Sources and evidence dates

Mistral Nemo third-party catalog record (catalog, checked 2026-09-06)
Third-party model catalog methodology (methodology, checked 2026-09-06)
Mistral AI official model documentation (primary, checked 2026-09-06)