DeepSeek R1 (0528) vs GLM-5.1
Benchmarks, API pricing and specs, head to head. Data updated 2026-06-10.
DeepSeek R1 (0528)
67.8DeepSeek · May 2025
The open reasoning model that started it all — RL-trained chain-of-thought, MIT licensed, and a research landmark.
GLM-5.1
Z.ai (Zhipu) · Apr 2026
The April 2026 GLM refresh — the first open-weights model to take #1 on SWE-bench Pro, edging out Claude Opus 4.6.
The verdict
These two models don't yet share verified results on the benchmarks we track, so judge them on specs, pricing and intended use. DeepSeek R1 (0528) is about 2.2x cheaper per blended million tokens (3:1 input:output mix). GLM-5.1 also takes 200K of context versus 128K for DeepSeek R1 (0528).
Specs & pricing
| DeepSeek R1 (0528) | GLM-5.1 | |
|---|---|---|
| modhub Index | 67.8 | — |
| Input price / 1M | $0.55 | $1.4 |
| Output price / 1M | $2.19 | $4.4 |
| Context window | 128K | 200K |
| Max output | 64K | 128K |
| Open weights | yes (MIT) | yes (MIT) |
| Reasoning model | yes | yes |
| Multimodal input | text | text |
| Knowledge cutoff | Mar 2025 | Feb 2026 |
| Released | May 2025 | Apr 2026 |
| Example monthly cost* | $8.79 | $20.60 |
* 10M input + 1.5M output tokens per month at list prices, no caching. Green = better value on that row.
Frequently asked questions
- Which is better, DeepSeek R1 (0528) or GLM-5.1?
- These two models don't yet share verified results on the benchmarks we track, so judge them on specs, pricing and intended use. DeepSeek R1 (0528) is about 2.2x cheaper per blended million tokens (3:1 input:output mix). GLM-5.1 also takes 200K of context versus 128K for DeepSeek R1 (0528).
- Which is cheaper, DeepSeek R1 (0528) or GLM-5.1?
- DeepSeek R1 (0528) costs $0.55/$2.19 per million input/output tokens, while GLM-5.1 costs $1.4/$4.4. For a typical workload of 10M input and 1.5M output tokens per month, that's $8.79 versus $20.60.
- Which model is better for coding, DeepSeek R1 (0528) or GLM-5.1?
- We don't yet track SWE-bench Verified results for both models; check their individual pages for coding-related scores.