Compute Comparison
Mistral/DevstralEfficient

devstral-medium

coding
devstral-medium Pricing per 1M tokens
Input$0.400
Output$2.00
View Mistral
Context Window
128K
tokens
devstral-medium Max Output
16K
tokens
devstral-medium Throughput
150
tokens/sec
devstral-medium Latency P50
400ms
time to first token
About devstral-medium

devstral-medium is a efficient-tier language model from Mistral. devstral-medium is a proprietary model available exclusively through Mistral's API. The Devstral family is Mistral's model series for this capability tier.

devstral-medium is priced at $0.4/1M input tokens and $2/1M output tokens. Output tokens are 5× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, devstral-medium costs approximately $1.40 per 1,000 API calls. Mistral does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.4/1M input rate.

Its 128K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 400ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 150 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 16K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

devstral-medium is best suited to coding. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how devstral-medium compares to similar models across these dimensions.

devstral-medium sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, devstral-medium is only available via Mistral's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing devstral-medium to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (74/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.

devstral-medium Intelligence Score
74/100
74

Composite quality score based on reasoning, coding, and instruction-following benchmarks.

devstral-medium Pricing vs Efficient Tier
CheapestThis modelMost expensive
$0.030$0.400$1.50

Input price vs 217 efficient models tracked. Avg: $0.321/1M.

devstral-medium Capabilities
Vision / Images✗
Function Calling✓
Streaming✓
Prompt Caching✗
Reasoning / CoT✗
Open Weights✗
Computer Use✗
Web Search✗
devstral-medium Full Specifications
Model IDdevstral-medium
ProviderMistral
Model FamilyDevstral
TierEfficient
Context Window128,000 tokens (128K)
Max Output Tokens16,384 tokens
Input Price$0.4 / 1M tokens
Output Price$2 / 1M tokens
Cached Input PriceNot available
Throughput150 tokens/sec
Latency (P50)400ms
Intelligence Score74/100
LiveBench Score—
Cost per Task (LiveBench)—
Data Confidence—
HLE Score—
GPQA Diamond—
SWE-bench Score—
OSWorld Score—
BrowseComp Score—
Terminal-Bench Score—
AutoBench Score—
Parameters—
Vision SupportNo
Function CallingYes
StreamingYes
Reasoning ModelNo
Open WeightsNo
Data SourceStatic (verified)
Last Updated2026-07-01

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devstral-medium Frequently Asked Questions