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. | 671B | 744B |
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. | 128k | 200k |
| 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. | — | $1.00 |
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. | — | $3.20 |
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 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. | $0.27SiliconFlow | $0.60GMICloud |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | $1.92GMICloud |
| $104SiliconFlow · fp8 | $216StreamLake · fp8 | |
| Benchmarks | ||
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. | 68.4% | 77.8% |
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. | 57.8% | 73.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.7% | 86% |
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. | — | 28% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 74.9% | — |
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. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 50.4% |
| Overview | ||
| Company | DeepSeek | Z.ai |
| Release date | Sep 22 2025 | Feb 12 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5GLM-5vsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolGLM-5vsGPT-6.1 SolDeepSeek V3.1 TerminusvsGemini 4 ArgonGLM-5vsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenGLM-5vsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7GLM-5vsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4GLM-5vsMistral Large 4Frequently asked questions
GLM-5 leads DeepSeek V3.1 Terminus on 3 of the 3 benchmarks they both report (SWE-Bench Verified, SWE-Bench Multilingual, GPQA Diamond). Only GLM-5 has a verified first-party API price: $1.00 per million input tokens and $3.20 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3.1 Terminus. DeepSeek V3.1 Terminus shipped 143 days before GLM-5, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while GLM-5 has 744B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 200k (GLM-5).
On SWE-Bench Verified, GLM-5 leads at 77.8% vs DeepSeek V3.1 Terminus at 68.4%. On SWE-Bench Multilingual, GLM-5 leads at 73.3% vs DeepSeek V3.1 Terminus at 57.8%. On GPQA Diamond, GLM-5 leads at 86% vs DeepSeek V3.1 Terminus at 80.7%.