Intelligence
12.7
13
General reasoning and benchmark headroom.
LimitedMistral: Mistral Small 3 is a budget text-first model from mistralai with a fast runtime profile, standard context posture, and the clearest fit around long-context research / multimodal.
Benchmark blend
Dev workflow signal
Standard
Budget tier
Mistral: Mistral Small 3 currently reads as a budget text-first option with standard context and a fast runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
LimitedTTFT 0.32s
Above averageHow much prompt and task state can stay in view.
Situational$0.30 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed for efficient local deployment. The model achieves 81% accuracy on the MMLU benchmark and performs competitively with larger models like Llama 3.3 70B and Qwen 32B, while operating at three times the speed on equivalent hardware. [Read the blog post about the model here.](https://mistral.ai/news/mistral-small-3/)
mistralai text-first profile
Long-context research / Multimodal with standard context and fast runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Latency and throughput look responsive enough for interactive loops.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Current metadata points to a text-first profile rather than a broad multimodal one.
Context window is more comfortable for focused tasks than extremely long sessions.
Focused chat, retrieval-augmented flows, and narrower production tasks.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Formal reasoning, structured problem solving, and competition-style math.
Long-horizon execution quality and interactive benchmark evidence.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.