DeepSeek R1 Basic vs GLM-5.1
DeepSeek R1 Basic (2025) and GLM-5.1 (2026) are frontier-tier reasoning models from DeepSeek and Zhipu AI. DeepSeek R1 Basic ships a 160k-token context window, while GLM-5.1 ships a 200k-token context window. On pricing, DeepSeek R1 Basic costs $0.56/1M input tokens versus $0.98/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek R1 Basic is ~75% cheaper at $0.56/1M; pay for GLM-5.1 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 Basic | GLM-5.1 |
|---|---|---|
| Best for | reasoning-heavy apps | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Long context | Coding, RAG, and Agents |
| Context window | 160k | 200k |
| Cheapest output | $1.68/1M tokens | $3.08/1M tokens |
| Provider routes | 1 tracked | 5 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- DeepSeek R1 Basic has the lower cheapest tracked output price at $1.68/1M tokens.
- Local decision data tags DeepSeek R1 Basic for Long context.
- GLM-5.1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5.1 has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5.1 uniquely exposes Function calling, Tool use, and Structured outputs in local model data.
- Local decision data tags GLM-5.1 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek R1 Basic
$868
Cheapest tracked route/tier: Fireworks AI
GLM-5.1
$1,554
Cheapest tracked route/tier: Z.ai
Estimated monthly gap: $686. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- GLM-5.1 is $1.40/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5.1 adds Function calling, Tool use, and Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- DeepSeek R1 Basic is $1.40/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling, Tool use, and Structured outputs before moving production traffic.
Specs
Pricing and availability
| Pricing attribute | DeepSeek R1 Basic | GLM-5.1 |
|---|---|---|
| Input price | $0.56/1M tokens | $0.98/1M tokens |
| Output price | $1.68/1M tokens | $3.08/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 Basic | GLM-5.1 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: GLM-5.1, tool use: GLM-5.1, structured outputs: GLM-5.1, and code execution: GLM-5.1. Both models share reasoning mode, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek R1 Basic lists $0.56/1M input and $1.68/1M output tokens on the cheapest tracked provider, while GLM-5.1 lists $0.98/1M input and $3.08/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 Basic lower by about $0.71 per million blended tokens. Availability is 1 providers versus 5, so concentration risk also matters.
Choose DeepSeek R1 Basic when provider fit and lower input-token cost are central to the workload. Choose GLM-5.1 when coding workflow support, larger context windows, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, DeepSeek R1 Basic or GLM-5.1?
GLM-5.1 supports 200k tokens, while DeepSeek R1 Basic supports 160k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek R1 Basic or GLM-5.1?
DeepSeek R1 Basic is cheaper on tracked token pricing. DeepSeek R1 Basic costs $0.56/1M input and $1.68/1M output tokens. GLM-5.1 costs $0.98/1M input and $3.08/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 Basic or GLM-5.1 open source?
DeepSeek R1 Basic is listed under MIT. GLM-5.1 is listed under MIT. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, DeepSeek R1 Basic or GLM-5.1?
Both DeepSeek R1 Basic and GLM-5.1 expose reasoning mode. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, DeepSeek R1 Basic or GLM-5.1?
GLM-5.1 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek R1 Basic and GLM-5.1?
DeepSeek R1 Basic is available on Fireworks AI. GLM-5.1 is available on Z.ai, OpenRouter, Fireworks AI, Vercel AI Gateway, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-25. Data sourced from public model cards and provider documentation.