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This page is the single source of truth for the models Macroscope runs on and what their tokens cost. The rates below are raw model cost, so a model’s price is the same wherever you use it. Each product bills these rates under its own pricing — see Model Selection & Costs for Check Run Agents.

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Available Models

  • Default = the value used when the field is omitted.
  • Effort applies to Anthropic models only — GPT, Grok, and open-source models ignore it (shown as —). On those models every effort level collapses to the same variant, so it has no effect.
  • Newer Anthropic models (Opus 4.6 / 4.7 / 4.8 / 5 / 5.5, Sonnet 4.6 / 5, Fable 5 / 5.1) set thinking automatically, so their reasoning field is ignored (shown as auto).
  • Writing a level a model does not support, such as xhigh on gpt-5-2 or max on kimi-k3, falls back to low and surfaces a warning.
Open-source models are opt-in and typically cost a fraction of the frontier options — a good fit for specialized, high-volume, or narrowly-scoped work, while Opus remains the default where the strongest model matters.
Open-source models are served through Fireworks AI’s hosted inference — Macroscope does not run local or self-hosted models, and you cannot point at your own endpoints. As we expand the set of inference providers we support, capacity fluctuations from these providers (during heavy load) mean latency may occasionally be higher due to request queuing.

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Reasoning and Effort

  • reasoning accepts off | low | medium | high | xhigh | max
  • effort accepts low | medium | high
Which field applies depends on the model:
  • Anthropic — effort always applies. claude-opus-4-5 and claude-sonnet-4-5 additionally honor reasoning, which maps to extended thinking; newer Anthropic models set thinking automatically and ignore reasoning.
  • OpenAI, xAI, and open source — reasoning selects the thinking level; effort is ignored.
Writing a reasoning level a model does not support falls back to low and surfaces a warning. For how to set these fields on a Check Run Agent, see Model Selection & Costs.

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Pricing

Raw model cost, USD per 1M tokens. The four columns are the four token counts a request is metered on, and they do not overlap — each token is counted once:
  • Input — prompt tokens read fresh, with no cache involved.
  • Output — everything the model generated, reasoning tokens included.
  • Cache write — prompt tokens written into the provider’s prompt cache. — means the provider charges nothing to create a cache entry.
  • Cached input — prompt tokens read back out of the cache instead of re-read.
Model ids are written here the way you set them in configuration. A provider’s own usage view may spell the same model differently — gpt-5-6-sol is gpt-5.6-sol there, grok-4-6 is grok-4.6, and the open-source models appear under vendor paths such as moonshotai/Kimi-K3.
Large prompts bill at a higher rate on some models. Crossing the threshold reprices the whole request, not just the tokens past it. “Prompt tokens” means uncached input plus cached input plus cache writes.
  • gpt-5-4, gpt-5-5, gpt-5-6-sol, gpt-5-6-terra, gpt-5-6-luna, gpt-6-astra, gpt-6-sol, gpt-6-luna — above 272,000 prompt tokens: 2x input, 1.5x output, and 2x on both cache columns where they are charged.
  • claude-sonnet-4-5 — above 200,000 prompt tokens: 2x input, 1.5x output, 2x cache write, 2x cached input.
  • grok-4-6 — at 200,000 prompt tokens or more: 2x on every token, output included.
Every other model above bills the same rate at any prompt size.
deepseek-v4-1-flash pricing changes on October 1, 2026. New rates, USD per 1M tokens:Input: $0.30, Output: $1.20, Cached input: $0.01The table above shows the rates in effect until then.