GPT-5.4 mini vs GPT-6 Luna
GPT-5.4 mini (Azure AI Foundry, 272,000-token context) versus GPT-6 Luna (Azure AI Foundry, 922,000-token context). GPT-6 Luna is cheaper by 89% on a blended token mix. Across 4 public benchmarks we tracked, GPT-5.4 mini wins 2 and GPT-6 Luna wins 2. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — GPT-5.4 mini vs GPT-6 Luna
GPT-5.4 mini and GPT-6 Luna target overlapping workloads but differ sharply on economics. GPT-6 Luna runs roughly 89% cheaper on a blended input-plus-output token mix, which translates to approximately $4,350 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
GPT-6 Luna ships a 922,000-token context window, 3.4x larger than GPT-5.4 mini's 272,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 272,000 tokens, the extra context on GPT-6 Luna is insurance you may never use — and GPT-5.4 mini may win on other axes.
Across 4 public benchmarks, GPT-5.4 mini leads on 2 and GPT-6 Luna leads on 2. The widest gap is on arena-webdev, where GPT-6 Luna scores 181.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gpt-6-luna
provider: azure-ai-foundry
fallback:
model: gpt-5-4-mini
provider: azure-ai-foundry
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| GPT-5.4 mini | GPT-6 Luna | |
|---|---|---|
| Input price | $0.750/M | $0.1000/M |
| Output price | $4.50/M | $0.500/M |
| Context window | 272,000 | 922,000 |
| Max output | 128,000 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | GPT-5.4 mini | GPT-6 Luna | Delta |
|---|---|---|---|
| Startup 10K requests/day | $495 /mo | $60.00 /mo | $435/mo |
| Mid-market 100K requests/day | $4,950 /mo | $600 /mo | $4,350/mo |
| Enterprise 1M requests/day | $49,500 /mo | $6,000 /mo | $43,500/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — GPT-6 Luna runs ~89% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — GPT-6 Luna fits 922,000 tokens versus the other model's 272,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
On arena-webdev, GPT-6 Luna scores 181.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | GPT-5.4 mini | GPT-6 Luna | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1447.0 | 1441.0 | GPT-5.4 mini | +6.0 |
| arena-elo-coding | 1495.0 | 1505.0 | GPT-6 Luna | +10.0 |
| arena-vision | 1250.0 | 1240.0 | GPT-5.4 mini | +10.0 |
| arena-webdev | 1397.0 | 1578.0 | GPT-6 Luna | +181.0 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 239% when moving from GPT-5.4 mini (272,000) to GPT-6 Luna (922,000). Re-check any prompt that relies on cramming long history or documents.
How to A/B test GPT-5.4 mini vs GPT-6 Luna in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark GPT-5.4 mini primary, mirror 20% of traffic to GPT-6 Luna in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — GPT-5.4 mini vs GPT-6 Luna
Which is cheaper, GPT-5.4 mini or GPT-6 Luna? ▾
GPT-6 Luna is cheaper by roughly 89% on a blended input + output token mix. Input prices are $0.750/M for GPT-5.4 mini versus $0.1000/M for GPT-6 Luna; output prices are $4.50/M versus $0.500/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of GPT-5.4 mini versus GPT-6 Luna? ▾
GPT-5.4 mini supports up to 272,000 tokens of context. GPT-6 Luna supports up to 922,000 tokens. GPT-6 Luna has the larger window by a factor of 3.4x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do GPT-5.4 mini and GPT-6 Luna both support tool calling? ▾
Yes — both GPT-5.4 mini and GPT-6 Luna support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
Both GPT-5.4 mini and GPT-6 Luna support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose GPT-5.4 mini over GPT-6 Luna? ▾
On the data this page surfaces, GPT-5.4 mini is the right pick when GPT-6 Luna's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose GPT-6 Luna over GPT-5.4 mini? ▾
You're cost-sensitive at scale — GPT-6 Luna runs ~89% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — GPT-6 Luna fits 922,000 tokens versus the other model's 272,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. On arena-webdev, GPT-6 Luna scores 181.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
How do I A/B test GPT-5.4 mini against GPT-6 Luna in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.