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Claude Opus 4.5 vs GLM-5

Claude Opus 4.5 (2025) and GLM-5 (2026) are frontier-tier reasoning models from Anthropic and Zhipu AI. Claude Opus 4.5 ships a 200K-token context window, while GLM-5 ships a 200k-token context window. On SWE-bench Pro, Claude Opus 4.5 leads by 3.2 pts. On pricing, GLM-5 costs $0.72/1M input tokens versus $5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

GLM-5 is ~594% cheaper at $0.72/1M; pay for Claude Opus 4.5 only for coding workflow support.

Specs

Released2025-11-012026-02-11
Context window200K200k
Parameters744B total, 40B active
Architecturedecoder onlymixture of experts
LicenseProprietaryMIT
Knowledge cutoff2025-12-

Pricing and availability

Claude Opus 4.5GLM-5
Input price$5/1M tokens$0.72/1M tokens
Output price$25/1M tokens$2.3/1M tokens
Providers

Capabilities

Claude Opus 4.5GLM-5
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

BenchmarkClaude Opus 4.5GLM-5
SWE-bench Pro41.838.6

Deep dive

On shared benchmark coverage, SWE-bench Pro has Claude Opus 4.5 at 41.8 and GLM-5 at 38.6, with Claude Opus 4.5 ahead by 3.2 points. The largest visible gap is 3.2 points on SWE-bench Pro, 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 vision: Claude Opus 4.5, multimodal input: Claude Opus 4.5, and code execution: Claude Opus 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 Opus 4.5 lists $5/1M input and $25/1M 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 $9.81 per million blended tokens. Availability is 6 providers versus 5, so concentration risk also matters.

Choose Claude Opus 4.5 when coding workflow support 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.

FAQ

Which has a larger context window, Claude Opus 4.5 or GLM-5?

Claude Opus 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 Opus 4.5 or GLM-5?

GLM-5 is cheaper on tracked token pricing. Claude Opus 4.5 costs $5/1M input and $25/1M 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 Opus 4.5 or GLM-5 open source?

Claude Opus 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 Opus 4.5 or GLM-5?

Claude Opus 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 Opus 4.5 or GLM-5?

Claude Opus 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 Opus 4.5 and GLM-5?

Claude Opus 4.5 is available on Microsoft Foundry, Anthropic, GCP Vertex AI, AWS Bedrock, and OpenRouter. 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.