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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-05-11 · Source: arcee_ai_reference_catalog
Code generation, refactoring and review, and developer-tooling workloads with large context.
Coder‑Large is a 32 B‑parameter offspring of Qwen 2.5‑Instruct that has been further trained on permissively‑licensed GitHub, CodeSearchNet and synthetic bug‑fix corpora. It supports a 32k context window, enabling mul...
coder-large is a Code model from Arcee Ai (US). HotON.ai tracks it at $0.50 per 1M input tokens and $0.80 per 1M output tokens, with a 33K-token context window. Its composite efficiency score is 88/100 at an estimated $0.001 per successful task.
coder-large is tracked at $0.50 per 1M input tokens and $0.80 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.73 per 1M tokens. Figures are illustrative demo data.
Code generation, refactoring and review, and developer-tooling workloads with large context.
coder-large supports up to a 33K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, coder-large is cheaper than 44% of models on input price and ranks #320 of 535 by overall efficiency.
Yes — qwen3-coder-flash is a lower-cost option at $0.98 per 1M output tokens, while still covering similar Code use cases. Compare them side by side on HotON.ai.
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HotON.ai — coder-large (Arcee Ai): $0.50/1M input, $0.80/1M output, as of 2026-05-11. https://hoton.ai/en/models/arcee-ai-coder-largePricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.