DeepSeek R1 vs GLM-5
DeepSeek R1 (2025) and GLM-5 (2026) are frontier-tier reasoning models from DeepSeek and Zhipu AI. DeepSeek R1 ships a 128k-token context window, while GLM-5 ships a 200k-token context window. On SWE-bench Verified, GLM-5 leads by 28.6 pts. On pricing, DeepSeek R1 costs $0.10/1M input tokens versus $0.60/1M 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 is ~500% cheaper at $0.10/1M; pay for GLM-5 only for long-context analysis.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | GLM-5 |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 128k | 200k |
| Cheapest output | $0.30/1M tokens | $2.08/1M tokens |
| Provider routes | 14 tracked | 7 tracked |
| Shared benchmarks | 2 rows | SWE-bench Verified leader |
Decision tradeoffs
- DeepSeek R1 has the lower cheapest tracked output price at $0.30/1M tokens.
- DeepSeek R1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek R1 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek R1 for Coding, RAG, and Agents.
- GLM-5 holds a shared-benchmark lead on SWE-bench Verified, ahead by 28.6 points.
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags GLM-5 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
$155
Cheapest tracked route/tier: Bitdeer AI
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $845. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, OpenRouter, and Together AI; start route-level A/B tests there.
- GLM-5 is $1.78/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution before moving production traffic.
- GLM-5 adds Function calling and Tool use in local capability data.
- Provider overlap exists on OpenRouter, Together AI, and Fireworks AI; start route-level A/B tests there.
- DeepSeek R1 is $1.78/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- DeepSeek R1 adds Code execution in local capability data.
Specs
Pricing and availability
| Pricing attribute | DeepSeek R1 | GLM-5 |
|---|---|---|
| Input price | $0.10/1M tokens | $0.60/1M tokens |
| Output price | $0.30/1M tokens | $2.08/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 | GLM-5 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 | GLM-5 |
|---|---|---|
| SWE-bench Verified | 49.2 | 77.8 |
| Google-Proof Q&A | 71.5 | 86.0 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek R1 at 49.2 and GLM-5 at 77.8, with GLM-5 ahead by 28.6 points; Google-Proof Q&A has DeepSeek R1 at 71.5 and GLM-5 at 86, with GLM-5 ahead by 14.5 points. The largest visible gap is 28.6 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on function calling: GLM-5, tool use: GLM-5, and code execution: DeepSeek R1. Both models share reasoning mode and structured outputs, 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 lists $0.10/1M input and $0.30/1M output tokens on the cheapest tracked provider, while GLM-5 lists $0.60/1M input and $2.08/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 lower by about $0.88 per million blended tokens. Availability is 14 providers versus 7, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose GLM-5 when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek R1 or GLM-5?
GLM-5 supports 200k tokens, while DeepSeek R1 supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, DeepSeek R1 or GLM-5?
DeepSeek R1 is cheaper on tracked token pricing. DeepSeek R1 costs $0.10/1M input and $0.30/1M output tokens. GLM-5 costs $0.60/1M input and $2.08/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 or GLM-5 open source?
DeepSeek R1 is listed under MIT. GLM-5 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 or GLM-5?
Both DeepSeek R1 and GLM-5 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 or GLM-5?
GLM-5 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 and GLM-5?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.