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. | 80B | 6B |
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 | 2k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $0.072DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.88DeepInfra | — |
| $73.00DeepInfra · fp8 | — | |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Sep 11 2025 | Mar 14 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3-NextvsClaude Haiku 5.5ChatGLM-6BvsClaude Haiku 5.5Qwen3-NextvsGPT-6.1 TerraChatGLM-6BvsGPT-6.1 TerraQwen3-NextvsGemini 4 CarbonChatGLM-6BvsGemini 4 CarbonQwen3-NextvsMuse Spark OpenChatGLM-6BvsMuse Spark OpenQwen3-NextvsGrok 4.7ChatGLM-6BvsGrok 4.7Qwen3-NextvsDeepSeek-V4.1-Flash-1017ChatGLM-6BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3-Next and ChatGLM-6B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. ChatGLM-6B shipped 912 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.
Qwen3-Next has 80B parameters, while ChatGLM-6B has 6B. Context windows are 256k (Qwen3-Next) vs 2k (ChatGLM-6B).
Direct benchmark comparisons are unavailable — Qwen3-Next and ChatGLM-6B don't publish scores on any of the same benchmarks.