mistral-small-4 API — compare providers
The same model (mistral-small-4) is served by 2 providers; Mistral AI is the cheapest at $0.75/1M blended — the priciest (Venice) costs 25% more.
| Rank | Provider | Input /1M | Output /1M | Blended /1M | Speed (tok/s per $) |
|---|---|---|---|---|---|
| 1 | Mistral AI | $0.15 | $0.6 | $0.75 ← cheapest | 195.3 |
| 2 | Venice | $0.1875 | $0.75 | $0.938 | — |
Speed per dollar = measured output tokens/s ÷ blended price. Throughput is provider-specific, so it can rank endpoints differently from price — see the speed-per-dollar ranking.
Prices are per 1M tokens (USD). "Blended" = input + output, for coarse ranking. Same underlying model, 2 serving providers, 25% spread top to bottom. Context window served: 262,144 tokens (32,768 max output) on all 2 priced endpoints — see how it ranks in biggest context windows. See how this compares across the catalogue in same model, different price.
mistral-small-4 spec sheetmaker-declared
What Mistral AI declares about the model itself — read from its own weights repository and documentation, not from any host. What each endpoint actually serves is in the pricing above: the two can differ, and both are true.
- Context window
- 262,144 tokens
- Parameters
- 119,000,000,000≈ 119.0B
- Active experts
- 4 of 128 active per tokenmaker's docs · read 2026-09-07
- Weight precision
- to verify
- License
- Apache 2.0 — open weights, permissivecurated · read 2026-09-07Scope: the licence on the published weights, and nothing else. It is not the maker's acceptable-use policy, which is a separate document, and it is not the contract you sign with whichever host you call — hosts set their own terms, which this index does not read.
- Knowledge cutoff
- not published by the maker
- Modalities
- text · image → textmaker's docs · read 2026-09-07
- Max output (official)
- not published by the maker
- Release date
- March 16, 2026
- Maker lifecycle status
- General availability
docs.mistral.ai · maker's documentation · read 2026-09-07 · 7 of 10 fields published by the maker
Speed by provider measured
Identical weights, different machines: the endpoint decides how fast the model answers. Our own measurements — a fixed prompt, streamed, 256 output tokens — taken from eu-paris and us-east.
| Provider | Blended /1M | eu-paris | us-east | ||
|---|---|---|---|---|---|
| TTFT p50 | tok/s | TTFT p50 | tok/s | ||
| Mistral AI | $0.75 | 216 ms | 147 | 340 ms | 147 |
TTFT = time to first token, p50 over 12 samples per endpoint and per region; tok/s = output tokens per second while streaming. Last measured: eu-paris 2026-09-06, us-east 2026-09-06. 1 of the 2 providers serving mistral-small-4 is in the bench — the others have no figure here, which says nothing about their speed. Latency moves with load and route: read it as an order of magnitude, not a guarantee. See the measurement protocol and the cross-catalogue speed-per-dollar ranking.
Cost per 1,000 requests by workload
What each provider actually bills for a representative job, not just the sticker price.
| Provider | Chatbot 1000 in / 500 out | RAG / long context 8000 in / 500 out | Batch summarize 4000 in / 1000 out |
|---|---|---|---|
| Mistral AI | $0.45 | $1.50 | $1.20 |
| Venice | $0.56 | $1.88 | $1.50 |
Estimate your own workload
Input tokens/request: Output tokens/request: Requests:
| Provider | Estimated cost (USD) |
|---|
Self-host / quantized builds
Open weights under Apache 2.0 — you can run mistral-small-4 on your own hardware instead of paying per token. Even quantized to int4 it needs roughly 71.4 GB of VRAM — a multi-GPU workstation or a large-memory Mac, not a typical desktop. Formats: GGUF for llama.cpp / Ollama / LM Studio, AWQ and GPTQ for GPU serving, MLX for Apple silicon.
- GGUF builds llama.cpp · Ollama · LM Studio
- AWQ builds GPU serving (vLLM)
- GPTQ builds GPU serving
- MLX builds Apple silicon
- Ollama library one-command local run
Links search the live model hubs (Hugging Face, Ollama) so they track new builds as they appear — we don't host weights. Which formats exist depends on what the model owner and community have published.
→ See VRAM, GPU and cost vs API for mistral-small-4, or the full self-host break-even calculator.