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Version 1.1.0
GPT-5.6 Sol · Evaluations · Kaino
GPT-5.6 Sol logo
model evaluation

GPT-5.6 Sol

OpenAI

OpenAI’s GPT-5.6 flagship model for advanced reasoning, coding, and complex agentic work.

modelgpt-5.6-sol
84.1KAINO SCORERecommended
Evaluated Jul 30, 202613 reviews
Website Docs GitHub

Scorecard

PricingMultimodalCostDev expTechnicalSpeedCodingReasoningRiskAdoption
  • Coding & agentic95
  • Technical capability92
  • Developer experience88
  • Reasoning & knowledge87
  • Pricing clarity86
  • Adoption signal84
  • Speed & availability78
  • Risk & evidence76
  • Multimodal & I/O74
  • Cost effectiveness63

Kainotomic evaluation

GPT-5.6 Sol has strong official-provider support in the supplied evidence: OpenAI docs and product pages identify it as a flagship GPT-5.6 model, with API availability, a 1.05M-token context window, 128k max output tokens, prompt caching, persisted reasoning, tool calling, multi-agent beta, and safety-system-card coverage. Pricing is also clear in the cited metadata at $5/M input, $30/M output, and $0.50/M cached input, though this is high versus many alternatives. Coding and agentic evidence is the strongest part of the record. DeepSWE/DataCurve lists gpt-5.6-sol[max] at 73%±3%, second behind Claude Opus 5[max], while OpenAI reports 72.7%. SWE-bench evidence from OpenLM.ai and LLM Boss reports 96.2 on SWE-bench Verified, but these are secondary leaderboard sources and should not be over-weighted as official SWE-bench publication evidence. Artificial Analysis reports a Coding Agent Index lead at 80 and Intelligence Index figures of 54 medium / 59 max. Public preference and performance evidence is positive but mixed in quality. Arena.ai/LMArena-derived sources place GPT 5.6 Sol xHigh at rank 2 with about 10.1% net improvement across many sessions. Artificial Analysis reports 63.8 tokens/s and 4.39s TTFT for medium, suggesting usable but not exceptional latency. LiveCodeBench was checked and did not list GPT-5.6/Sol. Terminal-Bench/Aider-specific benchmark results were not provided; GitHub/OpenAI only supports general developer signal, not benchmark performance.

Strengths

  • Very large 1.05M-token context window and 128k max output tokens in supplied OpenAI model evidence
  • Strong coding-agent evidence from DeepSWE/DataCurve and reported SWE-bench Verified scores
  • Clear API pricing and cached-input pricing in supplied sources
  • Good developer-facing feature set: tool calling, prompt caching, persisted reasoning, multi-agent beta
  • System-card and deployment-safety sources are present

Caveats

  • High token prices reduce cost-effectiveness despite strong capability
  • SWE-bench claims come from OpenLM.ai and LLM Boss rather than a directly supplied official SWE-bench source
  • LiveCodeBench was checked but no GPT-5.6/Sol listing was found
  • Terminal-Bench/Aider-specific results are not evidenced; GitHub source is only a weak developer-signal proxy
  • Multimodal capability is under-specified in the supplied evidence