Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 123B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 256k | — |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.25 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $2.50 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.20 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.40Mistral | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.00Mistral | — |
| $180Mistral | $375SpaceXSI list price | |
| Benchmarks | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 72.2% | 76.7% |
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 56% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 61.3% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 32.6% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 53.3% |
| Overview | ||
| Company | Mistral | SpaceXSI |
| Release date | Dec 9 2025 | Feb 17 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Devstral 2 (123B)vsClaude Haiku 5.5Grok 4.20 BetavsClaude Haiku 5.5Devstral 2 (123B)vsGPT-6.1 SolGrok 4.20 BetavsGPT-6.1 SolDevstral 2 (123B)vsGemini 4 ArgonGrok 4.20 BetavsGemini 4 ArgonDevstral 2 (123B)vsMuse Spark OpenGrok 4.20 BetavsMuse Spark OpenDevstral 2 (123B)vsDeepSeek-V4.1-Flash-1017Grok 4.20 BetavsDeepSeek-V4.1-Flash-1017Devstral 2 (123B)vsKimi K3Grok 4.20 BetavsKimi K3Frequently asked questions
Grok 4.20 Beta leads Devstral 2 (123B) on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only Grok 4.20 Beta has a verified first-party API price: $1.25 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Devstral 2 (123B). Devstral 2 (123B) shipped 70 days before Grok 4.20 Beta, so benchmark comparisons should account for the intervening progress.
Devstral 2 (123B) is open weight, while Grok 4.20 Beta is closed.
On SWE-Bench Verified, Grok 4.20 Beta leads at 76.7% vs Devstral 2 (123B) at 72.2%.