Mistral Large 2

On a typical 70% input / 30% output mix, Mistral Large 2 blends to $3.20 per million tokens. That puts it above 47% of the 47 models tracked here, and 3 other models in the same mid-range tier undercut it. Tier is a capability label, not a price band — the spread inside one tier is often wider than the gap between tiers, which is why picking by tier alone tends to overspend.

Input runs $2.00 per million and output $6.00 — a 3.0x ratio. That ratio matters more than either number on its own: a summarisation or classification workload reads far more than it writes and will track the input price, while a code-generation or long-form writing workload inverts that and will track the output price. Compare models on the ratio your own traffic actually has, not on the input price alone.

The benchmark profile is unusually flat — data extraction leads at 87/100 but only 5 points separate the strongest category from the weakest. That makes Mistral Large 2 a safe default for mixed workloads where you cannot predict in advance which capability a request will lean on, and a poor choice if you need a specialist.

The context window is 128K tokens and the throughput score is 82/100. Context only earns its keep if you fill it, and filling it is also what makes a request expensive — a large window is an option, not a discount. For a mid-range-tier model, judge the speed score against what you are doing: it decides everything for interactive chat and almost nothing for overnight batch work.

Worth checking before you commit: GPT-6 Luna scores at least as well on combined coding and reasoning and blends to $0.22 per million against Mistral Large 2's $3.20. That does not make Mistral Large 2 the wrong choice — context window, latency, vendor lock-in, and specific capabilities all sit outside a benchmark score — but it does mean the price difference needs a reason behind it.

The closest comparison is Gemini 1.5 Pro from Google, at $2.38 per million against $3.20. 16 models in this tier score higher on combined coding and reasoning, which is the useful framing: the question is rarely whether a model is good, it is whether it is the cheapest thing that is good enough for the specific work you are sending it.