OpenAI prices GPT-6.1 Sol at $2/1M input and $10/1M output tokens, the same as GPT-6 Sol and Claude Sonnet 5.5, and says it performs well on safety tests
Dave Treadwell, Amazon's SVP, emailed engineering staff: “The availability of the site has not been good recently.” …
Context & Ripple Effects
OpenAI had already moved its Sol line down from the temporary GPT-5.6 Sol price cut to GPT-6 Sol’s $2 per million input-token and $10 per million output-token rate. GPT-6.1 Sol retains that rate rather than repricing the tier.
The contrast is with the higher-priced GPT-6 Astra tier, set at $10/$50 per million tokens. OpenAI’s safety-test claim adds a deployment-oriented message to the lower-cost Sol offering, though the supplied material does not provide test details.
First-order effects
- OpenAI API customers can evaluate GPT-6.1 Sol without a higher per-token budget than GPT-6 Sol, while weighing OpenAI’s stated safety-test performance in model selection.
- GPT-6.1 Sol keeps OpenAI aligned with the $2/$10 pricing named for Claude Sonnet 5.5, making capability and operational fit more prominent differentiators at that price point.
Second-order effects
- Anthropic faces a more direct comparison at the same listed token price: buyers choosing between Sol and Claude Sonnet 5.5 can focus on coding, computer-use, and safety requirements rather than a price gap.
- The wide gap between Sol’s $2/$10 rate and Astra’s earlier $10/$50 rate gives OpenAI a clearer tiering structure, putting pressure on premium models to justify their higher inference spend.
Third-order effects
- If lower-priced models continue to approach premium-tier capability, AI buyers will increasingly evaluate providers by cost per useful task rather than token price alone.
- Safety testing is becoming part of the commercial positioning of general-purpose models, alongside price and task performance, as vendors seek enterprise deployment workloads.
The trend: Frontier-model vendors are pairing lower inference prices with stronger task and safety positioning, intensifying competition around effective cost per useful task.