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Last scan 2026-08-18 Models tracked 217 Providers 31 Cheapest paid Granite 4.0 H Micro $0.017/Mtok in Every price links to its source

GPT-5.6 Luna vs Qwen3.5-27B

Side-by-side comparison of API pricing, specs, benchmarks, and capabilities

GPT-5.6 Luna is 45.5% cheaper on blended cost ($0.450 vs $0.825/Mtok)
Specification
Overview
StatusCurrent mid tier Current mid tier Open weights
Released Jul 9, 2026 Feb 25, 2026
Pricing per million tokens
Input $0.200/Mtok $0.300/Mtok
Output $1.20/Mtok $2.40/Mtok
Blended avg $0.450/Mtok $0.825/Mtok
Cached input $0.020/Mtok
Price basis Alibaba Model Studio International list price, single 0<Token≤256K tier, from the vendor's open-source Qwen table. Thinking-mode output is billed at the same rate as non-thinking. No context-cache or batch rate is printed for this SKU, so cached input is left unset rather than assumed. Gateway resellers quote lower routed rates ($0.195/$1.56). This SKU's row carries no discount label, so the standard list rate is the one tracked here — the same basis as every other Alibaba row on this site.
Specifications
Context window 1M tokens 262K tokens
Parameters Proprietary 27B
Speed (TPS)
Modalities
Input
textimage
text
Benchmarks sources: Vellum LLM Leaderboard / Artificial Analysis
Independent composite scores measured for both models — each on its own scale
AA-Omniscience Index (−100–100) -10.3 -44
AA Intelligence Index 52.3
AA Agentic Index 46.9
GDPval-AA v2 1578.3 ELO
LMSYS Chatbot Arena 1408 ELO
Per-benchmark results published for GPT-5.6 Luna only — no independent per-benchmark scores exist for Qwen3.5-27B yet
Avg benchmark score 90.7
Perf / dollar 201.6
GPQA Diamond 92.3
SWE-Bench Verified 93
Terminal-Bench 2.1 84.3
HumanEval 93
Providers
Available from
OpenAI — $0.200/$1.20/Mtok
Alibaba — $0.300/$2.40/Mtok

Cost at scale

1M tokens · 50/50 input/output
Projected cost of GPT-5.6 Luna vs Qwen3.5-27B at increasing token volumes
VolumeGPT-5.6 LunaQwen3.5-27BSavings
1M tokens $0.45 $0.83 $0.37 (44.8%)
10M tokens $4.5 $8.25 $3.75 (45.5%)
100M tokens $45 $82.5 $37.5 (45.5%)
1000M tokens $450 $825 $375 (45.5%)

When to pick which

distilled from the pricing and spec data above
Pick GPT-5.6 Luna if…
  • cost dominates: $0.450/Mtok blended vs $0.825 — 45.5% less on the same 50/50 token mix
  • your workload re-reads context (agents, RAG, long chats): cached input costs $0.020/Mtok — 90% off its list input price, a discount Qwen3.5-27B doesn't offer
  • you need the longer context: 1M tokens vs 262K (4×)
Pick Qwen3.5-27B if…
  • you want open weights — self-host it, fine-tune it, or exit the API entirely; GPT-5.6 Luna is closed

Price movement

recorded changes since we began tracking each model · last checked Aug 18, 2026
Recorded price changes for GPT-5.6 Luna and Qwen3.5-27B — how many moves, the direction and date of the latest one, and its old → new prices per million tokens
Model Changes Latest move Old → new $/M
GPT-5.6 Luna 1 price cut on Aug 1, 2026 $1/$6 → $0.2/$1.2/Mtok
Qwen3.5-27B 0 — no move Stable since we began tracking — no change recorded
Only GPT-5.6 Luna has changed price since we began tracking; Qwen3.5-27B has held steady. See all recent price moves → MODELPRICEWATCH.COM · 2026-08-18
Try GPT-5.6 Luna on OpenAI

The cheaper option here — GPT-5.6 Luna costs $0.450/Mtok blended on OpenAI.

Get API key →

Summary

GPT-5.6 Luna by OpenAI costs $0.200/Mtok input and $1.20/Mtok output, with a 1M-token context window. It supports text, image input.

Qwen3.5-27B by Alibaba costs $0.300/Mtok input and $2.40/Mtok output, with a 262K-token context window. It supports text input.

On a blended cost basis, GPT-5.6 Luna is 45.5% cheaper than Qwen3.5-27B.It also has a larger context window.

The two aren't directly comparable on average benchmark score: GPT-5.6 Luna has published per-benchmark results, while Qwen3.5-27B does not yet — it is measured today on independent composites (see the table above).

Note: Pricing is per million tokens. Actual costs vary with usage patterns, prompt caching, and batch discounts. Always verify against official provider pricing pages.

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