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.
- Transcribe 1 ProFish AudioRemove
- Perceptron Mk1.5PerceptronRemove
- GPT-6 Luna ProOpenAIRemove
- MiniMax M2MiniMaxRemove
4 is the maximum. Remove one to add another.
| Attribute | Transcribe 1 Protranscribe-1-pro | Perceptron Mk1.5perceptron-mk1.5 | GPT-6 Luna Progpt-6-luna-pro | MiniMax M2minimax-m2 |
|---|---|---|---|---|
| Pricing | ||||
| Input | — Not priced per input token | $0.15 / 1M | $0.10 / 1M | $0.15 / 1M |
| Output | — Not priced per output token | $1.50 / 1M | $0.50 / 1M | $0.45 / 1M |
| Cache Write (5m) | Not applicable | $0.15 / 1M | $0.10 / 1M | $0.15 / 1M |
| Cache Write (1h) | Not applicable | $0.15 / 1M | $0.10 / 1M | $0.15 / 1M |
| Cache Read | Not applicable | $0.15 / 1M | $0.10 / 1M | $0.15 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | N/A | 36.9K | 1.1M | 196.6K |
| 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 | Yes | Yes | Yes |
| Catalogue | ||||
| Provider | Fish Audio | Perceptron | OpenAI | MiniMax |
| Category | voice | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-09-24 | 2026-09-25 | — | — |
| Description | ||||
| Summary | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier. | MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns. |