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Model ComparisonUpdated 2026-09

Gemini 3.7 Flash vs GPT-5.6 Luna: Choosing Between the Two Cheapest Tiers (2026)

Which cheap model for bulk work? Luna at $0.18/$1.08 versus Flash at $0.75/$3.75 — a 4× gap, but they are not the same kind of model. Classification versus reasoning, settled.

TL;DR: **GPT-5.6 Luna at $0.18/$1.08 is the cheapest non-Chinese model we sell**; **Gemini 3.7 Flash at $0.75/$3.75** costs about 4× more. They are not competitors: Luna is a fast lightweight tier (classification, intent detection, short summaries, formatting), while Flash is a **reasoning model** — it burns thinking tokens before answering — with a 1M context window and multimodal input. **Rule: if the task is 'recognize / classify / extract' → Luna, cheap enough to run freely. If it needs to 'think first' or swallow a large context → Flash.** ⚠️ With Flash, set max_tokens generously (≥1000) — otherwise the thinking budget is consumed before any answer is produced and you get an empty response.

What a 4× gap actually means

Say you are labeling sentiment on 100,000 product reviews: ~100 input tokens and ~10 output tokens each, so roughly 10M input + 1M output. **That is about $2.9 on Luna versus $11.3 on Flash.** For 'simple task × huge volume', picking the cheap tier is not penny-pinching — it is keeping budget for the work that actually needs capability. Invert it, though, and using a budget model for judgment-heavy work costs more in rework than it saves in tokens.

The hidden cost of reasoning models

Reasoning models like Gemini 3.6/3.7 Flash and the GLM-5.x line **burn 'thinking' tokens before answering, and those bill as output**. We have measured roughly 100 thinking tokens spent on a question as trivial as '1+1'. Two consequences: ① per-call cost runs higher than your estimate based on visible answer length; ② **setting max_tokens too low (say 8) truncates before thinking finishes and returns empty content** — not a model failure, a parameter mistake. For bulk work that needs no reasoning, a non-reasoning model is both cheaper and less fiddly.

A practical tiering rule

Split tasks by 'does this require judgment?'. **No judgment** (format conversion, field extraction, template generation, classification) → cheapest tier available. **Judgment required** (quality assessment, multi-condition reasoning, context-dependent summarization) → Flash or above. Most teams land at 60-70% in the first bucket; downgrading just that share cuts total spend by roughly a third.

How to call them

base_url https://cocodot.co/api/ai/v1, model `gpt-5.6-luna` (code mog-8-l) or `gemini-3.7-flash` (code mgg-13). Both fall within the $0.5 trial credit, so you can validate before topping up.

Two budget tiers (cocodot pricing, USD per M tokens)

GPT-5.6 LunaGemini 3.7 Flash
Input / Output$0.18 / $1.08$0.75 / $3.75
Typefast, lightweightreasoning (thinks first)
Contextstandard1M
Best forclassify / tag / extract / formatreasoning, long context, multimodal
Watch outnot for complex reasoninggive max_tokens room

FAQ

Anything cheaper?

Chinese budget tiers (DeepSeek Flash, the Qwen line) are excellent value on Chinese-language work. Same key — A/B them alongside.

Is Flash's 1M context useful?

Situationally. Great for QA over large documents, but long inputs bill too — a big context window is a capability, not a free lunch.

About cocodot

cocodot is a payment and AI access service for developers and cross-border teams in mainland China. It provides US-BIN virtual cards issued by a licensed institution — used to pay for overseas subscriptions and ad accounts — and an OpenAI-compatible AI API gateway for calling Claude, GPT and Gemini from within mainland China. Both share one wallet, funded by Alipay and accounted in USD. Card: $9.9 to open, 3% to load, 0% on spend, $1 per active card per month.

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Gemini 3.7 Flash vs GPT-5.6 Luna: Choosing Between the Two Cheapest Tiers (2026) · cocodot