Claude Opus 4.6 does not enable reasoning mode by default. To access its deep reasoning capabilities, users would typically need to call the native Claude API. To make this capability available through an OpenAI-compatible interface, we provide the claude-opus-4-6-think model, which has reasoning mode pre-enabled and a default 32k-token context window, allowing it to be called directly via the OpenAI unified API. Claude Opus 4.5 Think is a reasoning-focused variant of Claude Opus 4.6 designed for advanced tasks that require rigorous reasoning, complex decision-making, and long-chain analysis. Aside from its enhanced reasoning mechanism, all other capabilities remain consistent with the standard Claude Opus 4.6 model, making it well suited for complex engineering problem decomposition, multi-stage planning, and logic-intensive analysis.
claude-opus-4-6-think vs glm-5.2
Output tokens cost $25.00 per million on claude-opus-4-6-think and $2.76 per million on glm-5.2. Input tokens cost $5.00 per million on claude-opus-4-6-think and $0.79 per million on glm-5.2. Cached input tokens are billed at $0.50 per million on claude-opus-4-6-think and $0.20 per million on glm-5.2. Discounted rates are shown where a promotion is active. Context lengths are 200,000 tokens on claude-opus-4-6-think and 1,000,000 tokens on glm-5.2. Time to first token (TTFT) measured on AIHubMix is 1.7s on claude-opus-4-6-think and 1.1s on glm-5.2. Measured output throughput is 42.2 tok/s on claude-opus-4-6-think and 49.2 tok/s on glm-5.2. On the LMArena coding leaderboard claude-opus-4-6-think scores 1547 and glm-5.2 scores 1506.
GLM-5.2 is Z.ai’s flagship model for the era of long-horizon tasks. With a truly usable 1M-token context window, it can handle project-level engineering context, execute long-running tasks more reliably, follow engineering standards more consistently, and complete the full development workflow from requirements to multi-platform deployment in a single task.
Pricing & Specifications
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
Promotional prices show the discounted rate; see each model page for promotion windows.
Activity Past 30 Days
Daily traffic served through AIHubMix — how demand for each model is trending.
Tokens / day
Requests / day
Performance Past 3 Days
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
Throughput (tok/s)
TTFT (s)
Uptime (%)
LMArena Benchmarks
LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.
Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.
Cost calculator
Estimate your monthly bill for the same workload on each model.
Monthly = daily × 30. Discounted rates applied where a promotion is active.
FAQ
Which is cheaper: claude-opus-4-6-think, glm-5.2?
glm-5.2: $2.76/M output tokens; claude-opus-4-6-think: $25.00/M. Use the cost calculator above to estimate your own workload.
How do their coding arena scores compare?
claude-opus-4-6-think: 1547; glm-5.2: 1506 (LMArena coding leaderboard).
Which responds faster?
glm-5.2: 1.1s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
How large is each context window?
claude-opus-4-6-think accepts 200,000 and glm-5.2 accepts 1,000,000 input tokens. Maximum output per request is 32,000 tokens on claude-opus-4-6-think and 128,000 tokens on glm-5.2.
Which one generates tokens faster?
glm-5.2 at 49.2 tok/s and claude-opus-4-6-think at 42.2 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
What inputs and capabilities does each model support?
claude-opus-4-6-think accepts image and text input and supports thinking, tool calling, function calling and structured outputs; glm-5.2 accepts text input and supports thinking, tool calling, function calling and structured outputs.
Can I call claude-opus-4-6-think and glm-5.2 with the same API key?
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
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