Mistral Nemo Instruct 2407 TEE
chutes/nemo-instruct-2407-teePar chutes · famille: mistral-nemo · sorti 2024-07-01 · fin de connaissance: 2024-07
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Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacités
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding3
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents16
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency89
- Headline price (log-scaled)84/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.17 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.34 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.10 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.29 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.35 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Détail des tarifs
Tarif recommandé de chutes · unsloth/Mistral-Nemo-Instruct-2407-TEE
Disponible chez 1 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| Chutes chutes | unsloth/Mistral-Nemo-Instruct-2407-TEE | $0.025 | $0.098 | 131K | 2024-07-01 |
Frequently asked questions
How much does Mistral Nemo Instruct 2407 TEE cost?
Mistral Nemo Instruct 2407 TEE costs $0.025 per 1M input tokens and $0.098 per 1M output tokens, sourced from chutes. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Mistral Nemo Instruct 2407 TEE?
Mistral Nemo Instruct 2407 TEE has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Mistral Nemo Instruct 2407 TEE support tool calling?
No. Mistral Nemo Instruct 2407 TEE does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Mistral Nemo Instruct 2407 TEE support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Mistral Nemo Instruct 2407 TEE in our data source. Verify with chutes's official documentation before relying on it in production.
Can Mistral Nemo Instruct 2407 TEE accept image input?
No. Mistral Nemo Instruct 2407 TEE only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Mistral Nemo Instruct 2407 TEE open-weight?
Yes. Mistral Nemo Instruct 2407 TEE's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
Capability lists this model is in
Dernière mise à jour :
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.