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Provider Comparison

MiMo-V2.6-Pro vs. GPT-5.6 Luna (2026): The Duel

MiMo-V2.6-Pro vs. GPT-5.6 Luna (2026): intelligence 46.3 vs. 37.3, price per 1M tokens, 125 vs. 159 tok/s, ~1M context — plus the 42% rule.

Reviewed by Michael Kerkhoff, as of

Definition
Two fast, low-cost models in the same performance class, with different strengths. MiMo-V2.6-Pro leads the Artificial Analysis intelligence index for this pairing, 46.3 vs. 37.3. GPT-5.6 Luna counters with half the input price and roughly 27 percent more burst speed. Both offer about 1 million tokens of context — no tiebreaker there. The decision comes down to your pipeline's token mix.
Category
Provider Comparison
Options
MiMo-V2.6-Pro (Open Weight)GPT-5.6 Luna (API)

Detailed Comparison

A side-by-side analysis of key factors to help you make the right choice.

MiMo-V2.6-Pro (Open Weight) vs GPT-5.6 Luna (API)
FactorMiMo-V2.6-Pro (Open Weight)GPT-5.6 Luna (API)
Intelligence index (lead)46.3 points — clearly leads the pairing; the safer pick for dense, multi-step text tasks. Winner37.3 points — 9 behind, but fully adequate for simple extraction and classification.
Price per 1M input tokens$0.435 — moderate, but roughly twice Luna's input price.$0.20 — the clear input advantage for long documents and large corpora. Winner
Price per 1M output tokens$0.87 — cheaper output, worth it for extensive drafts. Winner$1.20 — pricier output, decisive drawback in output-heavy pipelines.
Burst speed125 tokens/s.159 tokens/s — about 27 percent faster, useful in sequential call chains. Winner
Context windowAbout 1M tokens — tie.About 1M tokens — tie, not a decision factor.
Deployment modelOpen weight — self-hostable, with your own cache and predictable per-GPU costs. WinnerManaged API with no hosting effort of your own.
Total Score · 1 ties3 / 62 / 6

Key Statistics

Real data from verified industry sources to support your decision.

All statistics come from verified third-party sources. Source, year, and direct link are shown on each metric.

When to Choose Each Option

Clear guidance based on your specific situation and needs.

Our Recommendation

Quality first: MiMo-V2.6-Pro. It leads the intelligence index by 9 points, has the cheaper output ($0.87 vs. $1.20 per 1M tokens) and, as an open-weight model, it can be self-hosted. Volume first: GPT-5.6 Luna. At $0.20 per 1M input tokens it is the better long-document processor, and at 159 vs. 125 tokens/s it is the faster model for sequential calls. The price crossover sits at 42 percent output share: above it MiMo is cheaper overall, below it Luna. Example: a report with 3,000 input and 400 output tokens costs about $0.00108 with Luna vs. $0.00165 with MiMo. A draft with 1,000 input and 1,500 output tokens costs $0.00174 with MiMo vs. $0.0020 with Luna. The context window (about 1M tokens each) does not separate them.

Choose MiMo-V2.6-Pro (Open Weight) when...
  • Answer quality decides — e.g., drafts and summaries of dense specialist texts.
  • Your pipeline has an output share above 42 percent of tokens — then MiMo is cheaper overall.
  • You want to self-host the open-weight model, on-prem or with your own cache.
Choose GPT-5.6 Luna (API) when...
  • You mostly process long inputs with short outputs — document extraction, classification, search indexing.
  • Speed is critical because calls run sequentially — Luna delivers about 27 percent more tokens per second.
  • You prefer a managed API without hosting yourself.

Common questions about this comparison answered.

Frequently Asked Questions

(01)Which model for daily text jobs like summarization, extraction and drafts?
Split by task, not by ranking: short input, long output (drafts) → MiMo-V2.6-Pro, because of $0.87 vs. $1.20 per 1M output tokens and +9 points in the intelligence index. Long input, short output (document extraction) → GPT-5.6 Luna, because of $0.20 vs. $0.435 per 1M input tokens.
(02)How is the 42-percent crossover calculated?
Per mixed token pair: MiMo charges $0.435 per 1M input and $0.87 per 1M output, Luna $0.20 and $1.20. Setting the costs equal and solving for the output share gives 41.6 percent. Above that share MiMo is cheaper, below it Luna.
(03)Why does an open-weight model lead an API model in the index?
In this class, open weight is no longer second choice: MiMo-V2.6-Pro scored 46.3 in the Artificial Analysis intelligence index (as of 22 Sep 2026), 9 points ahead of Luna. The index weights several reasoning benchmarks; the pure-speed point goes to Luna.
(04)Is the context window a differentiator?
No. Both models offer about 1 million tokens. With identical windows, price per token pair and response speed decide.

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