Standard Compute
Model comparison 2026

GPT-6 Luna vs Qwen3 235B A22B

GPT-6 Luna
OpenAI · Budget · Proprietary
$0.22/1M · 1.1M ctx
Qwen3 235B A22B
Alibaba (Qwen) · Budget · Open weights
$0.23/1M · 262K ctx

Community vote

Pick a winner in each dimension. You can change your vote any time.

Output Quality
Correctness and depth of what it produces
Agentic Ability
Tool calls, instruction-following, and multi-step tasks
Speed
Tokens per second and time-to-first-token
Value for money
How much capability you get per dollar
Reliability
Consistent results: fewer refusals, loops and format breaks

Specs and pricing side by side

Spec
GPT-6 Luna
Qwen3 235B
Maker
OpenAI
Alibaba (Qwen)
Blended price / 1M
$0.22
$0.23
Input / output
$0.10 in · $0.50 out / 1M tokens
$0.09 in · $0.55 out / 1M tokens
Context window
1.1M
262K
Open weights
No
Yes
Tool use
Yes
Yes
Reasoning
Yes
No
Output Quality
8.7
8.0
Agentic Ability
8.5
8.0
Speed
9.3
8.2
Value for money
9.4
10.0
Reliability
9.1
8.8

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: GPT-6 Luna or Qwen3 235B?

Updated 2026-10-13

Choose GPT-6 Luna if you want cheap, fast subtasks, routing and subagents in OpenAI-based stacks. Choose Qwen3 235B A22B if you want self-hosted or ultra-cheap open-weight agents.

In our editorial scoring, GPT-6 Luna leads in 4 of five dimensions (output quality, agentic ability, speed and reliability), while Qwen3 235B A22B leads in 1. On price, GPT-6 Luna runs about $0.22 per 1M tokens (blended) and is proprietary; Qwen3 235B A22B is about $0.23 and open-weight.

Where GPT-6 Luna falls short
  • Weak on long agentic runs (16.4% on Terminal-Bench 4.0)
  • Clear quality step below GPT-6.1 Sol
Full GPT-6 Luna breakdown →
Where Qwen3 235B falls short
  • Behind frontier models on quality
  • Needs real GPUs to self-host well
Full Qwen3 235B breakdown →

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