Model comparison
GPT-5.6 Luna vs Grok 4.6
OpenAI against SpaceXAI, compared on context, price, and verified benchmark results.
Catalog record checked August 12, 2026; individual provider fields may change.
OpenAI
GPT-5.6 Luna
Budget
SpaceXAI
Grok 4.6
Frontier
| Specification | GPT-5.6 Luna | Grok 4.6 |
|---|---|---|
| Provider | OpenAI | SpaceXAI |
| Tier | Budget | Frontier |
| Context window | Winner: 1.05M | 500K |
| Max output | 128K | Not verified |
| Input / 1M tokens | Winner: $0.20 | $2 |
| Output / 1M tokens | Winner: $1.20 | $6 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | none, low, medium, high, xhigh, max | low, medium, high, xhigh |
| Modalities | text, image | text, image |
| Released | July 9, 2026 | August 12, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-14) | 52 | Winner: 61 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GPT-5.6 Luna: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. · Grok 4.6: Base tier is $2/$6 per million tokens; prompts at or above 200K tokens are priced at $4/$12. Cached input is $0.50 / $1.00. Web/X search tool calls bill separately.
GPT-5.6 Luna
OpenAI's cheapest tier, built for high-volume work where unit cost matters more than frontier quality. From Aug 2026 also the ChatGPT Free/Go default with unlimited text chats + Think for harder questions.
Best for
- Classification
- Summarisation
- High-volume chat
- ChatGPT Free/Go default
Watch out
The bare `gpt-5.6` alias routes to Sol at 25x the input price — specify the full id. ChatGPT Free→Luna is consumer Chat only; Work/Codex/API Luna builds did not change with the Aug 6 Chat refresh.
Grok 4.6
SpaceXAI's current code and chat default — same $2/$6 list as Grok 4.5, with a 500K context window and image input.
Best for
- Coding agents
- Chat and knowledge work
- Cost-sensitive frontier work
Watch out
Token rates double above a 200K-token prompt. DeepSWE 1.1 best-effort for this family is the xhigh row at 66.7% (the catalog table rounds Pass@1 to 67%), not the higher-scoring medium config.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
When the cheaper one wins
GPT-5.6 Luna is cheaper on output at $1.20 per million tokens against $6 for Grok 4.6 — about 5.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, GPT-5.6 Luna is 67% Pass@1 at $3.03/task versus Grok 4.6 at 67% / $5.5/task. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerCommon questions
GPT-5.6 Luna vs Grok 4.6
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.6 Luna or Grok 4.6 cheaper for input?
GPT-5.6 Luna is cheaper at $0.20 per million input tokens, against $2 for Grok 4.6 — roughly 10× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Luna has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. Grok 4.6 has tiered pricing: Base tier is $2/$6 per million tokens; prompts at or above 200K tokens are priced at $4/$12. Cached input is $0.50 / $1.00. Web/X search tool calls bill separately.
Is GPT-5.6 Luna or Grok 4.6 cheaper for output?
GPT-5.6 Luna is cheaper at $1.20 per million output tokens, against $6 for Grok 4.6 — roughly 5.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Luna has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. Grok 4.6 has tiered pricing: Base tier is $2/$6 per million tokens; prompts at or above 200K tokens are priced at $4/$12. Cached input is $0.50 / $1.00. Web/X search tool calls bill separately.
Which has the larger context window, GPT-5.6 Luna or Grok 4.6?
GPT-5.6 Luna accepts 1.05M tokens against 500K for Grok 4.6. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.6 Luna and Grok 4.6 support the same reasoning levels?
GPT-5.6 Luna exposes none, low, medium, high, xhigh, max, while Grok 4.6 exposes low, medium, high, xhigh.
Should I use GPT-5.6 Luna or Grok 4.6?
GPT-5.6 Luna is the budget tier and Grok 4.6 the frontier tier. The useful question is whether your hardest task actually fails on the cheaper one — most production volume such as classification, extraction and summarisation does not.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.