DeepSeek-R1 Base vs GLM-4.6 Chat: which model should you choose?
We compared DeepSeek-R1 Base and GLM-4.6 Chat on price, context window and 12 benchmarks. DeepSeek-R1 Base has the stronger average benchmark score; GLM-4.6 Chat is cheaper on a blended 3:1 basis.
DeepSeek-R1 Base
DeepSeek-R1 Base is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.05 per million input tokens and $26.24 per million output tokens, and averages 80.1 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $26.24 / 1M
- Context
- 400,000
- Avg score
- 80.1
- Open weights
- Yes
GLM-4.6 Chat
GLM-4.6 Chat is a multimodal model from Zhipu AI. It offers a 400,000-token context window, costs $4.89 per million input tokens and $21.46 per million output tokens, and averages 79.8 across the index's benchmark suite.
- Input
- $4.89 / 1M
- Output
- $21.46 / 1M
- Context
- 400,000
- Avg score
- 79.8
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Base | GLM-4.6 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $4.89 | B |
| Output price / 1M | $26.24 | $21.46 | B |
| Context window | 400,000 | 400,000 | Tie |
| Max output | 65,536 | 16,384 | A |
| Average score | 80.1 | 79.8 | A |
| Arena Elo | 1442.9 | 1437.7 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Base wins 7, GLM-4.6 Chat wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | GLM-4.6 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 82.2 | 1.2 | GLM-4.6 Chat |
| GPQA Diamond | 76.2 | 72.8 | 3.4 | DeepSeek-R1 Base |
| SWE-bench Verified | 71.2 | 66.0 | 5.2 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 86.3 | 2.9 | GLM-4.6 Chat |
| MATH-500 | 84.9 | 86.8 | 1.9 | GLM-4.6 Chat |
| AIME 2025 | 73.8 | 79.2 | 5.4 | GLM-4.6 Chat |
| LiveCodeBench | 76.0 | 69.6 | 6.4 | DeepSeek-R1 Base |
| IFEval | 88.2 | 87.8 | 0.4 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 83.2 | 1.0 | GLM-4.6 Chat |
| HellaSwag | 90.0 | 89.9 | 0.1 | DeepSeek-R1 Base |
| MMMU | 74.2 | 73.5 | 0.7 | DeepSeek-R1 Base |
| Arena Elo | 1442.9 | 1437.7 | 5.2 | DeepSeek-R1 Base |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and GLM-4.6 Chat costs about $9.03. GLM-4.6 Chat is roughly 13% cheaper than DeepSeek-R1 Base. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 400,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 7, GLM-4.6 Chat wins 5, and 0 are tied. On the index average, DeepSeek-R1 Base leads GLM-4.6 Chat by 0.3 points (80.1 versus 79.8).
The price picture favours GLM-4.6 Chat. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with DeepSeek-R1 Base for quality-sensitive work and GLM-4.6 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Base if
- You want the higher average benchmark score in this index.
- You need the larger 65,536-token output limit.
Choose GLM-4.6 Chat if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-R1 Base better than GLM-4.6 Chat?
On the index average, DeepSeek-R1 Base leads GLM-4.6 Chat by 0.3 points (80.1 versus 79.8). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 7 benchmarks, GLM-4.6 Chat wins 5.
Which is cheaper, DeepSeek-R1 Base or GLM-4.6 Chat?
GLM-4.6 Chat is cheaper on a blended 3:1 basis at about $9.03 per million tokens, versus $10.35 for DeepSeek-R1 Base.
Which has the larger context window?
Both models list a 400,000-token context window.
Are these models open weights?
DeepSeek-R1 Base is open weights; GLM-4.6 Chat is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 Base for quality-sensitive work and GLM-4.6 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.