Is GPT-5.6 Luna cheaper than Grok 4.5?

On input tokens, yes: Luna is $1/MTok vs Grok 4.5 at $2/MTok. On output tokens, they tie at $6/MTok. For a typical 30K-input / 5K-output coding task, Luna costs $0.06 and Grok 4.5 costs $0.09. But 'cheaper' depends on token efficiency: Grok 4.5 uses roughly 2x fewer tokens per completed task on agentic benchmarks (e.g., ~16K output tokens per SWE-Bench Pro task vs 60K+ for verbose models). Once you factor task-completion tokens, the two are close to par for agentic coding — Luna wins on simple/short tasks, Grok 4.5 wins on long agentic runs.

Grok 4.5 or Luna for coding?

Grok 4.5 for agentic coding, Luna for high-volume simple coding. Grok 4.5 was trained in collaboration with Cursor on real developer interaction data and scores 83.3% on Terminal-Bench 2.1 — top-tier for its price band. Luna scores ~65% on Terminal-Bench 2.1 and drops sharply on multi-step tool use. Concretely: if your workload is 'agent runs tool calls in a terminal and completes tasks', Grok 4.5. If your workload is 'high-throughput code generation, batch tasks, autocomplete', Luna.

Which model is faster, Luna or Grok 4.5?

Grok 4.5 has published output speeds of 80-91 tokens/sec — among the fastest at this price tier. Luna is optimized as OpenAI's fastest tier but doesn't publish comparable per-second numbers; anecdotal reports put it in the same 60-100 tok/s band via the OpenAI API. For latency-critical UX (in-app AI features, autocomplete), both are viable — measure on your infrastructure. Grok 4.5 has an edge on time-to-first-token for tool-heavy workloads because it emits fewer intermediate 'thinking' tokens.

Should I switch from Sonnet 5 to Luna or Grok 4.5?

Not for the same task type — they solve different problems. Sonnet 5 ($2/$10 intro) has a 1M context window and leads on structured reasoning + code review. Luna and Grok 4.5 don't compete there. Where the switch makes sense: if you're using Sonnet 5 for simple/high-volume generation (autocomplete, boilerplate, batch), move to Luna (60% cheaper output) or Grok 4.5 (40% cheaper output + faster). Keep Sonnet 5 for review, long-context RAG, and structured reasoning.

What's the catch with Luna at $1/$6?

Two catches. First, quality variance — Luna's benchmark scores drop 8-12 points from Terra on hard tasks. On simple tasks the drop is negligible, but the variance is workload-dependent. Test task success rate on your actual prompts before switching. Second, Luna is optimized as OpenAI's fastest/cheapest tier, which usually means smaller model + more aggressive quantization. That trade shows up in edge cases: complex tool schemas, dense system prompts, and multi-turn conversations where context accuracy matters. For 90% of high-volume workloads Luna is fine; for the other 10% you'll see silent failures.

Quick Answer

GPT-5.6 Luna vs Grok 4.5: The Cheap-Tier Frontier Showdown (July 2026)

Published: July 11, 2026

The Setup

Two name-brand models arrived within a week of each other, both undercutting the previous frontier price floor:

Grok 4.5 (SpaceXAI, launched July 8-9, 2026): $2 input / $6 output per MTok, 500K context, #4 on the Artificial Analysis Intelligence Index
GPT-5.6 Luna (OpenAI, rolling out July 2026): $1 input / $6 output per MTok, the efficiency tier of the GPT-5.6 family

Both are the answer to “what if a frontier lab shipped a model priced against DeepSeek?”

Price Per Task

Model	Input	Output	30K in / 5K out	Notes
Luna	$1	$6	$0.06	Cheapest name-brand model
Grok 4.5	$2	$6	$0.09	Cheaper on output-heavy tasks vs Luna once token efficiency is factored
Sonnet 5 (intro)	$2	$10	$0.11	Ends Aug 31 → $3/$15
Terra	$2.50	$15	$0.15	GPT-5.5-level everyday tier
Gemini 3.1 Pro	$2	$12	$0.12	2M context
Sol	$5	$30	$0.30	Flagship

Luna wins on raw per-token cost. But token efficiency matters more than sticker price for anything agentic.

Token Efficiency: The Real Grok 4.5 Story

Artificial Analysis measured token usage per completed task:

Model	Output tokens per SWE-Bench Pro task
Grok 4.5	~16,000
Sonnet 5	~35,000
Opus 4.8	~67,000

Grok 4.5 uses roughly 2x fewer output tokens than Sonnet 5 and 4x fewer than Opus 4.8 on the same task. That’s the difference between “cheap sticker price” and “cheap actual bill.”

Luna’s token efficiency numbers aren’t published in comparable form, but as an efficiency-tier model with aggressive summarization, expect it to sit in the 15-25K range for similar tasks — competitive with Grok 4.5 on the raw token count.

Coding Benchmarks

Benchmark	Grok 4.5	Luna (est.)
Terminal-Bench 2.1	83.3%	~65%
SWE-bench Pro	64.7%	~48%
DeepSWE 1.0	62.0%	~40%
Coding Agent Index (AA)	76	~55

Grok 4.5 is a clear coding winner at this price tier. The gap is real: 15-20 percentage points on agentic coding benchmarks.

Luna’s advantage is in tasks that don’t need agentic depth — batch generation, autocomplete, classification.

When Luna Beats Grok 4.5

High-throughput simple tasks — autocomplete, boilerplate, extraction. Luna’s $1 input is meaningfully cheaper for input-heavy workloads.
In-app AI features with strict latency budgets and simple prompts
Batch generation where token cost dominates and per-task quality is homogeneous
OpenAI ecosystem lock-in — if your infra is built around OpenAI’s tools/SDKs, Luna is the drop-in cheap tier

When Grok 4.5 Beats Luna

Agentic coding — terminal tool use, multi-step debugging, refactors. Grok 4.5’s Cursor-collab training shows here.
Cursor users — Grok 4.5 is integrated as a routed model option
Long agentic runs — token efficiency compounds; Luna’s per-token savings evaporate over 50+ tool calls
Front-end code generation with visual realism — Grok 4.5 has an edge in code that reflects design intent

The Split-Traffic Recommendation

For most teams, “either/or” is the wrong framing. The routing pattern:

Task → Model
├── Autocomplete → Luna
├── Simple generation → Luna
├── Batch code tasks → Luna
├── Extraction / classification → Luna
├── Agentic coding → Grok 4.5
├── Terminal agent → Grok 4.5
├── Cursor Composer routing → Grok 4.5
└── Code review / reasoning → Sonnet 5

This split typically saves 30-50% vs “all Sonnet 5” while preserving quality on the tasks that matter.

Availability & Access

Grok 4.5:

Available via SpaceXAI API (x.ai)
Integrated in Cursor as a routed model option
Public GA July 9, 2026

GPT-5.6 Luna:

Rolling out via OpenAI API through July 2026
Available in ChatGPT (tier-appropriate)
Confirmed pricing: $1 input / $6 output per MTok

Risks & Caveats

Luna quality variance: 8-12 point benchmark drops on hard tasks. Test on your actual prompts.
Grok 4.5 policy/moderation: SpaceXAI’s content policies are looser than OpenAI/Anthropic. If your product needs strict moderation, factor that in.
Both are new: Grok 4.5 is 3 days old at time of writing (July 11, 2026). Luna is rolling out. Real-world evals are limited.
Cursor bias: If you’re not a Cursor user, some of Grok 4.5’s training advantage doesn’t apply to your workflow.

The Bottom Line

Luna ($1/$6) is the new cheap-tier price floor for a name-brand model. Use it for high-volume simple tasks.

Grok 4.5 ($2/$6) is the best agentic coding model at this price band. Use it for terminal agents, Cursor, and coding tasks that need tool use.

They’re not really competing for the same workload. Route by task type, not by picking a winner.