DeepSeek Prover V2 vs 
GLM 5.3 Prime
DeepSeek Prover V2 is cheaper than GLM 5.3 Prime at $0/M vs $2.8/M input tokens.
Interactive Catan Board
Hex grid you can resize with a slider. Geometry plus state.
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Which answer wins?
Price and specs
| Spec | DeepSeek Prover V2 | GLM 5.3 Prime |
|---|---|---|
| Input price | Free | $2.8/M tokens |
| Output price | Free | $8.8/M tokens |
| Context window | 164K tokens | 1.0M tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Sep 2026 |
| At 10M a month | $0 | $28.00 |
Input tokens at list price. No caching, no batch discount.
Where to run it
DeepSeek Prover V2
No hosts listed on OpenRouter.
GLM 5.3 Prime1 host
Per million tokens. Prices and uptime via OpenRouter, checked 28 Sep 2026.
Common questions
What is the difference between DeepSeek Prover V2 and GLM 5.3 Prime?
DeepSeek Prover V2 is developed by DeepSeek while GLM 5.3 Prime is developed by Z.ai. DeepSeek Prover V2 has a 164K token context window vs GLM 5.3 Prime's 1.0M. You can compare their actual outputs across 1 challenges on Rival to see how they differ in practice.
Which is better, DeepSeek Prover V2 or GLM 5.3 Prime?
It depends on your use case. DeepSeek Prover V2 and GLM 5.3 Prime each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 1 challenges so you can judge which fits your needs best.
How much does DeepSeek Prover V2 cost compared to GLM 5.3 Prime?
DeepSeek Prover V2 costs $0/M input tokens and GLM 5.3 Prime costs $2.8/M input tokens. DeepSeek Prover V2 is $2.80/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
How can I compare DeepSeek Prover V2 and GLM 5.3 Prime on Rival?
This page shows a side-by-side comparison of DeepSeek Prover V2 and GLM 5.3 Prime across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.
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