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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.

  1. GPT-6.1 SolOpenAIRemove
  2. MiniMax M2MiniMaxRemove
  3. Transcribe 1 ProFish AudioRemove
  4. Gemini 3.5 TranscribeGoogleRemove

4 is the maximum. Remove one to add another.

gpt-6.1-sol vs minimax-m2 vs transcribe-1-pro vs gemini-3.5-transcribe
AttributeGPT-6.1 Solgpt-6.1-solMiniMax M2minimax-m2Transcribe 1 Protranscribe-1-proGemini 3.5 Transcribegemini-3.5-transcribe
Pricing
Input$2.00 / 1M$0.15 / 1M— Not priced per input token— Not priced per input token
Output$10.00 / 1M$0.45 / 1M— Not priced per output token— Not priced per output token
Cache Write (5m)$2.00 / 1M$0.15 / 1MNot applicableNot applicable
Cache Write (1h)$2.00 / 1M$0.15 / 1MNot applicableNot applicable
Cache Read$2.00 / 1M$0.15 / 1MNot applicableNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M196.6KN/A98.3K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesNoYes
JSON ModeYesYesNoNo
StreamingYesYesNoNo
Catalogue
ProviderOpenAIMiniMaxFish AudioGoogle
Categorychatchatvoicevoice
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released——2026-09-242026-09-25
Description
SummaryGPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.