Mistral Nemo Instruct 2407
DigitalOcean Gradient chatMistral Nemo Instruct 2407 is a DigitalOcean Gradient chat model.It supports a 128,000-token context windowwith up to 512 output tokens.Input is priced at $0.300/M tokens and output at $0.300/M tokens. Route Mistral Nemo Instruct 2407 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Mistral Nemo Instruct 2407 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.3000/M input · $0.3000/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.
| Input | $0.300/M | |
| Output | $0.300/M |
Limits
- Context window
- 128,000 tokens
- Max input
- 128,000 tokens
- Max output
- 512 tokens
- Modalities
- text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Where it's strong
Watch out for
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Mistral Nemo Instruct 2407 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Mistral Nemo Instruct 2407 via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Mistral Nemo Instruct 2407 via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="gradient/mistral-nemo-instruct-2407",
messages=[{"role": "user", "content": "Hello, Mistral Nemo Instruct 2407!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="gradient/mistral-nemo-instruct-2407",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Same model on other providers
mistral-nemo-instruct-2407 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| OVHcloud AI | $0.130/M | $0.130/M | May 12, 2026 |
FAQ
How much does Mistral Nemo Instruct 2407 cost?
Input is priced at $0.300 per 1M tokens and output at $0.300 per 1M tokens (DigitalOcean Gradient, last verified May 12, 2026).
What is the context window of Mistral Nemo Instruct 2407?
Mistral Nemo Instruct 2407 supports a 128,000-token context window with up to 512 output tokens.
Does Mistral Nemo Instruct 2407 support function calling?
Mistral Nemo Instruct 2407 does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Mistral Nemo Instruct 2407 good for production?
Mistral Nemo Instruct 2407 is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Mistral Nemo Instruct 2407 with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Mistral Nemo Instruct 2407
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.