Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
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| Attribute | Qwen3 ASR 0.6Bqwen3-asr-0.6b | S1s1 | GPT-6 Lunagpt-6-luna | Claude Opus 5.5claude-opus-5.5 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0 / 1M | $0 / 1M | $0.10 / 1M | $4.00 / 1M |
| Output | $0 / 1M | $0 / 1M | $0.50 / 1M | $20.00 / 1M |
| Cache Write (5m) | Not applicable | Not applicable | $0.10 / 1M | $5.00 / 1M |
| Cache Write (1h) | Not applicable | Not applicable | $0.10 / 1M | $8.00 / 1M |
| Cache Read | Not applicable | Not applicable | $0.10 / 1M | $0.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | N/A | N/A | 1.1M | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | No | No | Yes | Yes |
| Function Calling | No | No | Yes | Yes |
| JSON Mode | No | No | Yes | Yes |
| Streaming | No | No | Yes | Yes |
| Catalogue | ||||
| Provider | Qwen | Fish Audio | OpenAI | Anthropic |
| Category | voice | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-08-13 | 2026-07-29 | — | — |
| Description | ||||
| Summary | Qwen3-ASR 0.6B speech-to-text. Billed per second of audio. | Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text. | GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability. | Claude Opus 5.5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows, succeeding Opus 5. It excels at multi-step changes across large codebases, code review and bug detection, financial and scientific analysis, and understanding dense charts, diagrams, and screenshots, with stronger grounding when reporting figures and citing sources. Compared with Opus 5, it completes comparable tasks with fewer steps and lower token usage while providing clearer, more concise progress reporting. With adaptive thinking and configurable effort levels, Opus 5.5 can balance reasoning depth, latency, and cost, making it well suited for both demanding autonomous workflows and latency-sensitive professional tasks. |