LLaMA 3.1vsQwen3.8-Max
LLaMA 3.1 | Qwen3.8-Max | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 2.4T |
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. | — | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
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 | $2.00Alibaba |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.04DeepInfra | $6.00Alibaba |
| Benchmarks | ||
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% | 94% |
| BenchmarksPublished by one model only | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 67.7% |
PaperBenchResearch reproduction — reproducing the results of an ML research paper end to end | — | 93% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 86.6% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 53.4% |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 86.1% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 88.4% |
BabyVisionVisual reasoning — Tests core visual reasoning — seeing and understanding images the way even young children can, which AIs often find surprisingly hard. Higher is better. | — | 82% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Jul 23 2024 | Aug 3 2026 |
| Access | Open Weight | Proprietary |
Other comparisons
LLaMA 3.1vsClaude Opus 5Qwen3.8-MaxvsClaude Opus 5LLaMA 3.1vsGPT-5.6 SolQwen3.8-MaxvsGPT-5.6 SolLLaMA 3.1vsGemini 3.7 FlashQwen3.8-MaxvsGemini 3.7 FlashLLaMA 3.1vsGrok 4.6Qwen3.8-MaxvsGrok 4.6LLaMA 3.1vsDeepSeek-V4-Pro-0813Qwen3.8-MaxvsDeepSeek-V4-Pro-0813LLaMA 3.1vsMistral Medium 3.5Qwen3.8-MaxvsMistral Medium 3.5
Frequently asked questions
Qwen3.8-Max leads LLaMA 3.1 on 1 of the 1 benchmark they both report (BullshitBench v2). LLaMA 3.1 shipped 741 days before Qwen3.8-Max, so benchmark comparisons should account for the intervening progress.
LLaMA 3.1 is open weight, while Qwen3.8-Max is proprietary.
On BullshitBench v2, Qwen3.8-Max leads at 94% vs LLaMA 3.1 at 14%.
LLaMA 3.1 was released by Meta on Jul 23 2024.
Qwen3.8-Max was released by Qwen on Aug 3 2026.
LLaMA 3.1 is an open weight model released by Meta. Qwen3.8-Max is a proprietary model released by Qwen.