Claude Haiku 5.5 vs Mistral Large 4

Where it leads

• Lower input and output prices, $0.10 and $0.50 vs $1.36 and $4.18 per 1M tokens
• Higher SimpleQA Verified score, 23.8% vs 20%
• Cheaper cached input, $0.01 vs $0.14 per 1M tokens
• Batch pricing, $0.05 input and $0.25 output per 1M tokens

Best for

• High-volume classification, extraction and routing under the 100,000-token line
• Cost-sensitive fan-out steps where a frontier model is overkill

Watch out for

• Priced by prompt length, and it is a cliff rather than a taper: a request whose prompt passes 100,000 tokens is billed at $0.50 input and $2.50 output for the whole request, five times the short rate
• Cache reads and cache writes both count toward that 100,000-token measurement, so a large cached prefix can tip a request into the higher band on its own
• It is the only current Claude model whose 1M window is not priced uniformly; every other 4.6-and-later model bills a 900k request at the same rate as a 9k one

Where it leads

No measured lead over Claude Haiku 5.5 in this data.

Best for

• European data-residency workloads that want a frontier-scale multimodal model
• Teams planning to self-host once the open weights ship

Watch out for

• This is a public preview: Mistral says the reinforcement learning run is still in flight and the model will keep changing
• Weights and their licence were not published at the time of this update, so it is recorded as API-only
• Mistral offered a temporary 50% launch discount; the recorded rates are the list prices
• Epoch AI measured 20.0% on SimpleQA Verified, low for its size, so check factual recall on your own questions