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 | 9B |
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 | 128k |
| 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 | Jun 5 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3-NextvsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Qwen3-NextvsGPT-6.1 TerraGLM-4-9BvsGPT-6.1 TerraQwen3-NextvsGemini 4 CarbonGLM-4-9BvsGemini 4 CarbonQwen3-NextvsMuse Spark OpenGLM-4-9BvsMuse Spark OpenQwen3-NextvsGrok 4.7GLM-4-9BvsGrok 4.7Qwen3-NextvsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3-Next and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4-9B shipped 463 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.
Qwen3-Next has 80B parameters, while GLM-4-9B has 9B. Context windows are 256k (Qwen3-Next) vs 128k (GLM-4-9B).
Direct benchmark comparisons are unavailable — Qwen3-Next and GLM-4-9B don't publish scores on any of the same benchmarks.