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. | 35B | 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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.375 | — |
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. | $2.25 | — |
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. | $0.01PrimeIntellect | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.092PrimeIntellect | — |
| $6.60PrimeIntellect · fp8 | — | |
| These models have no shared benchmark scores. | ||
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. | 49.5% | — |
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. | 73.4% | — |
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. | 67.2% | — |
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. | 51.5% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 21.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 86% | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 78% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 75.3% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81.7% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Apr 16 2026 | Jun 5 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3.6vsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Qwen3.6vsGPT-6.1 TerraGLM-4-9BvsGPT-6.1 TerraQwen3.6vsGemini 4 CarbonGLM-4-9BvsGemini 4 CarbonQwen3.6vsMuse Spark OpenGLM-4-9BvsMuse Spark OpenQwen3.6vsGrok 4.7GLM-4-9BvsGrok 4.7Qwen3.6vsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.6 and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.6 has a verified first-party price for an available API: $0.375 per million input tokens and $2.25 per million output tokens. No currently available pay-as-you-go API rate is tracked for GLM-4-9B. GLM-4-9B shipped 680 days before Qwen3.6, so benchmark comparisons should account for the intervening progress.
Qwen3.6 has 35B parameters, while GLM-4-9B has 9B. Context windows are 256k (Qwen3.6) vs 128k (GLM-4-9B).
Direct benchmark comparisons are unavailable — Qwen3.6 and GLM-4-9B don't publish scores on any of the same benchmarks.