GPT-5.6 Luna vs GPT-6 Astra Code Review Cost
Luna found 69 verified bugs to Astra's 92 in a 50-PR test. Compare security misses, methodology limits, live API rates, and routing advice.
By AI Pricing Guru Editorial Team
AI Pricing Guru articles are maintained by the editorial workflow behind the site: daily pricing snapshots, provider source checks, and review passes for model launches, subscription limits, and billing changes.
TL;DR
- Verdict: Luna is a plausible first-pass reviewer for routine changes, but this test does not support using it alone on authentication, authorization, or other high-risk code.
- Entelligence reports 69 verified bugs for Luna versus 92 for Astra across 50 pull requests; Luna used 3.6% of Astra's reported spend.
- Luna found 9 of 24 security bugs versus Astra's 19, and 74% of its findings were verified versus 96% for Astra.
- No OpenAI model or rate changed. Use the live prices below and route high-risk or failed reviews to a stronger model.
GPT-5.6 Luna vs GPT-6 Astra direct API price scale
USD per 1M tokens. Input and output rates are charted separately.
Estimate your own code-review token workload
Assumes 75% input tokens and 25% output tokens using current per-million rates.
GPT-5.6 Luna
openai
$4.50
- Input share
- $1.50
- Output share
- $3.00
GPT-6 Astra
openai
$200.00
- Input share
- $75.00
- Output share
- $125.00
Current OpenAI API rates used as the study's price basis
| Model | Provider | Input / 1M | Cached / 1M | Output / 1M |
|---|---|---|---|---|
| GPT-5.6 Luna | openai | $0.2 | $0.02 | $1.20 |
| GPT-6 Astra | openai | $10.00 | $1.00 | $50.00 |
Built from pricing.json at publish time.
An Entelligence comparison gives GPT-5.6 Luna a useful but limited role in code review: cheap first pass, not sole security gate. Luna found three-quarters as many verified bugs as GPT-6 Astra while using 3.6% of Astra’s reported spend, but it missed most of the benchmark’s security bugs.
This is a vendor-run third-party benchmark from a company that sells code-review and model-routing products, not an OpenAI launch or price cut. The live table, chart, and calculator above render maintained rates from pricing.json instead of freezing token prices in this article.
What the headline price means
The source’s “cheap model” headline refers to Luna’s Standard output rate per million tokens—not the price of one review or a subscription. Its reported review costs depend on prompt size, diff length, output, cache treatment, and token mix.
No canonical pricing update is needed. OpenAI’s official model pages match the live rates on our OpenAI pricing page and in the pricing API.
What the 50-PR benchmark found
Entelligence ran Luna and Astra on 50 intentionally defective public pull-request diffs: ten each from Cal.com, Sentry, Discourse, Keycloak, and Grafana. Both received the same bug-only prompt covering correctness, security, concurrency, resources, and error handling.
| Result reported by Entelligence | GPT-5.6 Luna | GPT-6 Astra |
|---|---|---|
| Verified bugs | 69 | 92 |
| Findings raised | 93 | 96 |
| Findings verified | 74% | 96% |
| Relative total run cost | 1x | 28x |
| Mean review time | 23 seconds | 36 seconds |
| Mean output tokens per review | 2,104 | 688 |
The models overlapped on 44 bugs. Astra alone found another 48, while Luna alone found 25. Running both found 117 of the 143-bug verified pool, so Luna was not merely a weaker copy of Astra.
Luna’s security gap changes the decision
On data and logic bugs, Luna found 39 versus Astra’s 47. On concurrency, it found 10 versus 13. Security produced the largest gap: Luna found 9 of 24 verified security bugs; Astra found 19.
Keycloak, an identity and access-management codebase, was Luna’s weakest repository. It found 6 verified bugs there versus Astra’s 14, and only half of its Keycloak findings survived verification.
Routine UI, data-flow, and low-risk application changes are the clearest Luna candidates. Authentication, permissions, secrets, payments, infrastructure, and boundary-crossing code should escalate automatically and retain deterministic scanners, tests, and human security review.
What the test can and cannot prove
Entelligence pooled findings from Luna, Astra, GPT-5.6 Sol, and its public reviewer, then used Astra and Sol as separate judges. A finding counted only when both judges agreed; they agreed on 91% of submitted findings.
That is stronger than accepting each model’s claims, but Astra was both contestant and judge. Most PRs were single runs. On ten repeated PRs, Astra re-found 67% of its initial verified bugs in both repeats, while Luna re-found 47%.
Every underlying PR predates both models’ training cutoffs. The defects were injected for the benchmark, but familiarity with the surrounding public code may still help. The test also lacks full repository context, production behavior, and a complete ground-truth bug list. Treat the result as routing evidence, not a universal recall score.
Entelligence links the public AI-Code-Review-Evals organization rather than one immutable result bundle. Anyone replaying the study should pin the exact artifact revision first.
Pricing impact: pay for risk, not every diff
The source’s per-review spend made Astra about 28 times costlier and its cost per verified bug about 20 times higher. That does not make Luna universally cheaper: false positives consume reviewer time, and one missed authorization bug can outweigh a large token saving.
The reported 50-PR bill was $0.20 for Luna versus $5.66 for Astra. That works out to $0.0041 versus $0.113 per review and $0.0030 versus $0.061 per verified bug in this specific harness—not a universal review price.
Use the token calculator for current rate math. For a provider control, compare Anthropic pricing with identical repository state, prompts, tools, and stopping rules. Then measure API tokens, retries, reviewer minutes, accepted findings, and escaped-defect severity.
A practical policy is Luna for routine first passes, premium escalation after a failed validator, and automatic stronger review for sensitive paths. Teams testing isolated review workers can compare DigitalOcean development infrastructure for disposable sandboxes; keep production credentials outside the reviewer.
Affiliate disclosure: AI Pricing Guru may earn a commission from the sponsored link above at no extra cost to you. It does not affect this analysis.
Labs decision: keep the external test separate
Luna is already represented in AI Pricing Guru Labs with 49 of 49 deterministic text tasks completed, while our current route is not authorized for Astra. We are not importing Entelligence’s counts into the 49-task leaderboard because its repository-diff harness, model judges, repeated-run design, and scoring target are different.
The Labs coverage note records the boundary. A comparable replay requires authorized Astra access, immutable prompts and outputs, a ground-truth defect set, an independent judge, complete token logs, and identical repository context.
Bottom line
GPT-5.6 Luna made cheap code review economically interesting, but it did not replace premium or human review. It captured 75% of Astra’s verified-bug count at a small fraction of the reported spend, then fell sharply behind on security and repeat consistency.
Route by risk: start cheap on routine diffs, escalate sensitive code and failed checks, and measure reviewer time plus escaped defects—not token price alone. For a broader comparison, see our best AI for coding guide.
Sources: Entelligence’s GPT-5.6 Luna vs GPT-6 Astra code-review study, the public AI-Code-Review-Evals organization, and OpenAI’s official GPT-5.6 Luna model page, GPT-6 Astra model page, and API pricing. Pricing, availability, benchmark claims, and linked artifacts checked September 14, 2026 at 21:36 UTC.