GPT-5.6 Luna review navigator
Official benchmarks, independent analysis, and community reports about GPT-5.6 Luna, clearly separated from Tabbit's own testing.
Media
2 source-checked resourcesGPT-5.6 Luna Benchmarks & Pricing (Public Benchmarks & Pricing)
BenchLM rates GPT-5.6 Luna at 67.3/100, ranking it 23 among 218 models. Its strongest area in the public evidence is Coding: 73.0 points, 6/135; Agentic ranks 43/130. The page also lists $0.20 per million input tokens, $1.20 per million output tokens, and cach。
GPT-5.6 Luna Semgrep IDOR Security Benchmark and Cost per True Positive
One-sentence takeaway Semgrep's security evaluation found that Luna costs roughly 6 times less per true positive than heavier models, with only a marginal F1 sacrifice; however, precision and recall are strongly dependent on the harness, so raw-model scores mu。
Community
10 source-checked resourcesGPT-5.6 Luna Is Really Underrated: Codex User Experience
The poster believes that GPT-5.6 Luna, at medium thinking, is close to GPT-5.4 mini in speed and capability, and can approach GPT-5.5 medium at high effort. In the comments, one user set Luna Max as the default model, while others considered Luna's quality and。
Thoughts after using GPT-5.6 Luna for 48 hours
This 48-hour experience report contrasts with the positive reviews from the Codex community: using GPT-5.6 Luna high in Hermes agent for personal-assistant tasks, the author found it “smart but slow,” prone to repeated iteration, quick to consume quota, occasi。
GPT-5.6 Luna Max vs. Sol Medium: An X User's Real-World Cost Test
Google's index summary for X shows that the author compared GPT-5.6 Luna Max with Sol Medium, saying that Luna used more tokens but averaged about $1.20 per session, while Sol averaged about $29. This result is useful as a community-tested lead suggesting that。
GPT-5.6 Sol, Terra, and Luna: Three-Tier Reddit Benchmarks and Routing Recommendations
The post compiles pricing and multiple benchmarks for the three model tiers and offers routing recommendations: use Terra as the default model for most workloads, reserve Sol for the hardest agentic/terminal tasks, and use Luna for high-frequency pipelines. Th。
I Benchmarked GPT-5.6 Sol/Luna/Terra by Role: Role-Based Evaluation
The author did not collapse the models into a single overall score, instead measuring them in roles such as strategic decision-making, repository execution, and code repair. The results show that Sol high is suited to the main strategic session, while Sol medi。
GPT-5.6 Luna and Gemini 3.6 Flash: A Cost Counterexample in Document-Vision Tasks
The post title uses a benchmark to claim that Luna is better than Google's models, but the comments provide an important production counterexample: in PDF document auditing, classification, question answering, and bounding-box tasks, one commenter considers Ge。
GPT-5.6 Luna vs. DeepSeek V4 Flash: Cache Hits and Real-World Task Costs
The discussion centers on whether “Luna outperforms DeepSeek V4 on performance and cost.” The original poster emphasizes a 99.9% cache hit rate on long tasks; respondents say Luna may use fewer tokens and run faster, but consume roughly 2–3 times as many dolla。
5.6 Luna Extra High is the work horse I needed
The author considers Luna Extra High a more practical workhorse on a Plus account: Sol is for “cracking hard problems” and making plans, while Luna Extra High is for day-to-day execution. The comments also include opposing experiences: one person says Luna x-h。
Agents on Rails: 8 Models, 21 Atomic Tasks
Google's index shows that the Rails team compared 8 models on 21 atomic tasks. The summary reports these results for Luna: 73% of runs succeeded with the default medium reasoning effort, the total cost of 63 runs was approximately 90 cents, and it was labeled 。
GPT-5.6 Luna Reddit Codex Quota and Cache Cost: A Hands-on Measurement
One-sentence takeaway A Luna-only Codex Plus session incurred an API-equivalent cost of approximately $2.43 based on 3.52M uncached input tokens, 73.79M cached input tokens, and 205K output tokens, yet consumed approximately 16–17% of the weekly quota. This sh。
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Official benchmarks, independent analysis, and community reports about GPT-5.6 Luna, clearly separated from Tabbit's own testing.