Claude Sonnet 4.5 vs GLM-5
Claude Sonnet 4.5 (2025) and GLM-5 (2026) are frontier-tier reasoning models from Anthropic and Zhipu AI. Claude Sonnet 4.5 ships a 200K-token context window, while GLM-5 ships a 200k-token context window. On pricing, GLM-5 costs $0.72/1M input tokens versus $3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5 is ~317% cheaper at $0.72/1M; pay for Claude Sonnet 4.5 only for vision-heavy evaluation.
Specs
| Released | 2025-09-29 | 2026-02-11 |
| Context window | 200K | 200k |
| Parameters | — | 744B total, 40B active |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | MIT |
| Knowledge cutoff | 2025-12 | - |
Pricing and availability
| Claude Sonnet 4.5 | GLM-5 | |
|---|---|---|
| Input price | $3/1M tokens | $0.72/1M tokens |
| Output price | $15/1M tokens | $2.3/1M tokens |
| Providers |
Capabilities
| Claude Sonnet 4.5 | GLM-5 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Claude Sonnet 4.5 and multimodal input: Claude Sonnet 4.5. Both models share reasoning mode, function calling, tool use, 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, Claude Sonnet 4.5 lists $3/1M input and $15/1M output tokens, while GLM-5 lists $0.72/1M input and $2.3/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GLM-5 lower by about $5.41 per million blended tokens. Availability is 8 providers versus 5, so concentration risk also matters.
Choose Claude Sonnet 4.5 when vision-heavy evaluation and broader provider choice are central to the workload. Choose GLM-5 when provider fit and lower input-token cost 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, Claude Sonnet 4.5 or GLM-5?
Claude Sonnet 4.5 supports 200K tokens, while GLM-5 supports 200k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Claude Sonnet 4.5 or GLM-5?
GLM-5 is cheaper on tracked token pricing. Claude Sonnet 4.5 costs $3/1M input and $15/1M output tokens. GLM-5 costs $0.72/1M input and $2.3/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Claude Sonnet 4.5 or GLM-5 open source?
Claude Sonnet 4.5 is listed under Proprietary. 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 vision, Claude Sonnet 4.5 or GLM-5?
Claude Sonnet 4.5 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Claude Sonnet 4.5 or GLM-5?
Claude Sonnet 4.5 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Claude Sonnet 4.5 and GLM-5?
Claude Sonnet 4.5 is available on Microsoft Foundry, Anthropic, Snowflake Cortex, GCP Vertex AI, and AWS Bedrock. 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: 2026-04-24. Data sourced from public model cards and provider documentation.