Compare AI models
| Specifications | ||
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1.05M |
| 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. | — | $2.00 |
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. | — | $10.00 |
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 third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.02DeepInfra | $1.00OpenAI |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.04DeepInfra | $5.00OpenAI |
These models have no shared benchmark scores. | ||
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. | 14% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 68.8% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 97% |
OSWorld 2.0Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | — | 60.5% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 56.4% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 33.2% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Jul 23 2024 | Sep 22 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Opus 5.5GPT-6 SolvsClaude Opus 5.5LLaMA 3.1vsGemini 3.8 FlashGPT-6 SolvsGemini 3.8 FlashLLaMA 3.1vsGrok 4.7GPT-6 SolvsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-FlashGPT-6 SolvsDeepSeek-V4.1-FlashLLaMA 3.1vsMistral Medium 3.5GPT-6 SolvsMistral Medium 3.5LLaMA 3.1vsKimi K3GPT-6 SolvsKimi K3Frequently asked questions
LLaMA 3.1 and GPT-6 Sol don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-6 Sol has a verified first-party API price: $2.00 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3.1. LLaMA 3.1 shipped 791 days before GPT-6 Sol, so benchmark comparisons should account for the intervening progress.
LLaMA 3.1 is open weight, while GPT-6 Sol is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.1 and GPT-6 Sol don't publish scores on any of the same benchmarks.