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Model comparison

GPT-6 Luna vs Qwen 3.8 27B

OpenAI against Qwen, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

OpenAI

GPT-6 Luna

Budget

vs

Qwen

Qwen 3.8 27B

Budget · Open weights

AI model capability comparison
SpecificationGPT-6 LunaQwen 3.8 27B
ProviderOpenAIQwen
TierBudgetBudget
Context windowWinner: 1.05M262K
Max output128KNot verifiedUnverified
Input / 1M tokensWinner: $0.10$0.50
Output / 1M tokensWinner: $0.50$3
WeightsClosedOpen
ParametersGPT-6 low-cost tier27B dense VLM (Gated DeltaNet hybrid)
Reasoning levelsnone, low, medium, high, xhigh, maxlow, medium, xhigh
Modalitiestext, imagetext, image, video
LicenseNot disclosedUnverifiedApache 2.0
API model idgpt-6-lunaqwen3.8-27b
ReleasedSeptember 22, 2026August 13, 2026
Artificial Analysis Intelligence Index [max] (2026-09-26)37.3Not verifiedUnverified
Artificial Analysis Intelligence Index [xhigh] (2026-09-26)Not verifiedUnverified33.7
DeepSWE 1.1 [max] (2026-09-22)66.6Not verifiedUnverified

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • 2026-09-26
    Artificial Analysis

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.

  • 2026-09-22
    OpenAI — Introducing GPT-6 Sol and Luna (vendor; not yet on Datacurve's board)

    Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.

    Directly comparable

Pricing tiers

GPT-6 Luna: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026.

Qwen 3.8 27B: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.

BudgetRecord checked September 26, 2026

GPT-6 Luna

GPT-6 Luna is OpenAI's cheapest current model at $0.10/$0.50 — the direct successor to GPT-5.6 Luna at half the price, with the full 1.05M-token context.

Best for

  • High-volume chat and extraction
  • Classification
  • Cost-capped agent loops

Watch out

Small tier: it matches GPT-5.6 Luna on the Artificial Analysis index but sits well below GPT-6 Sol on hard reasoning; the DeepSWE figure is vendor-reported at max effort.

BudgetOpen weightsRecord checked September 26, 2026

Qwen 3.8 27B

Qwen 3.8 27B is Alibaba's compact deployment-friendly dense VLM — 262K native context (1M via YaRN), Apache 2.0 weights, distinct from the hosted Qwen 3.8 Max API.

Best for

  • Self-hosting on a single node
  • Multimodal input at small scale
  • Long-context on modest hardware

Watch out

Dense 27B means higher memory per token than an MoE of equal active size; video input is multimodal-input only.

When the cheaper one wins

GPT-6 Luna is cheaper on output at $0.50 per million tokens against $3 for Qwen 3.8 27B — about 6.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.

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Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 4/5 core specs verified on both sides — Not published for at least one side: max output.
  • 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
  • Verified within the last 90 days — Newest catalog check was 2 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? GPT-6 Luna · Qwen 3.8 27B

Common questions

GPT-6 Luna vs Qwen 3.8 27B

Answered from the verified figures on this page rather than general guidance.

Is GPT-6 Luna or Qwen 3.8 27B cheaper for input?
GPT-6 Luna is cheaper at $0.10 per million input tokens, against $0.50 for Qwen 3.8 27B — roughly 5.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Luna has tiered pricing: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026. Qwen 3.8 27B has tiered pricing: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.
Is GPT-6 Luna or Qwen 3.8 27B cheaper for output?
GPT-6 Luna is cheaper at $0.50 per million output tokens, against $3 for Qwen 3.8 27B — roughly 6.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Luna has tiered pricing: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026. Qwen 3.8 27B has tiered pricing: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.
Which has the larger context window, GPT-6 Luna or Qwen 3.8 27B?
GPT-6 Luna accepts 1.05M tokens against 262K for Qwen 3.8 27B. This only matters if you routinely send very long documents or large codebases.
Do GPT-6 Luna and Qwen 3.8 27B support the same reasoning levels?
GPT-6 Luna exposes none, low, medium, high, xhigh, max, while Qwen 3.8 27B exposes low, medium, xhigh.
Should I use GPT-6 Luna or Qwen 3.8 27B?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. GPT-6 Luna suits high-volume chat and extraction; Qwen 3.8 27B suits self-hosting on a single node.
Can I self-host GPT-6 Luna or Qwen 3.8 27B?
Qwen 3.8 27B publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-6 Luna is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: GPT-6 Luna costs $0.10 per 1M tokens versus $0.50 for Qwen 3.8 27B — a 5x difference at the headline tier.
  • Context: GPT-6 Luna takes 1.05M against 262K for Qwen 3.8 27B — only decisive if your prompts approach the smaller window.
  • Measured capability: GPT-6 Luna leads Artificial Analysis Intelligence Index 37.3 to 33.7 (measured 2026-09-26).
  • Deployment: Qwen 3.8 27B publishes weights you can self-host; the other is API-only.
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