Punches far above its price: GPQA Diamond 92.3%, SWE-bench Pro 62.7%, Terminal-Bench 2.1 84.7%
$0.20/$1.20 per 1M after the July 30, 2026 price cut — dramatically cheaper per token than Gemini 3.6 Flash
Full 1.05M-token context at budget pricing — larger than most rival small models
Weaknesses
Long-context recall collapses at scale: 41.3% on 512K–1M token tasks vs Terra's 72.5%
Text and image input only — no video, audio, or native PDF ingestion like Gemini 3.6 Flash
Real-world use cases
What people actually use GPT-5.6 Luna for.
High-volume summarization and drafting at $0.20/1M input
Routine steps in agent pipelines where Sol/Terra would be overkill
Budget coding assistance — 62.7% SWE-bench Pro within ~2 points of Sol at 1/25th the output cost
How GPT-5.6 Luna compares
The nearest models people weigh against it, and what actually separates them.
vs GPT-3.5 Turbo — Against GPT-3.5 Turbo (OpenAI), GPT-5.6 Luna runs about 30% cheaper per token, takes 64.1x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs GPT-3.5 Turbo (older v0613) — Against GPT-3.5 Turbo (older v0613) (OpenAI), GPT-5.6 Luna runs about 53% cheaper per token, takes 256.4x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs GPT-3.5 Turbo Instruct — Against GPT-3.5 Turbo Instruct (OpenAI), GPT-5.6 Luna runs about 60% cheaper per token, takes 256.4x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
Price History
GPT-5.6 Luna pricing over time
↑100% since Aug 7
38 data points · tracked daily since Aug 7, 2026
Ready to try it?
Start using GPT-5.6 Luna
Cheap high-throughput summarization, drafting, and routine agent steps. Start free — no card required.
GPT-3.5 Turbo is OpenAI's legacy fast and affordable chat model, optimized for dialogue and straightforward text tasks at low cost. It was the backbone of early ChatGPT and remains a go-to for high-volume, cost-sensitive deployments.
Verdict
A once-dominant budget model now outclassed by cheaper, smarter alternatives like GPT-4o mini.
Quality score
35%
Pricing
$0.50/1M in
$1.50/1M out
Speed
Very fast
5/5 speed
Context
16k tokens
GPT-3.5 Turbo is still available via OpenAI API and supports fine-tuning, which keeps it relevant for teams with existing trained models. However, OpenAI has deprioritized its development in favor of the GPT-4o family. Not multimodal — text only.
BudgetLegacyFastHigh-volumeChatbot
Best for
High-volume, low-complexity tasks like chatbots, classification, summarization, and simple Q&A where cost matters more than cutting-edge quality.
An older versioned snapshot of GPT-3.5 Turbo (v0613), OpenAI's once-dominant mid-tier language model optimized for fast chat completions and instruction following. This specific checkpoint is frozen in time, predating later capability improvements introduced in subsequent GPT-3.5 Turbo updates.
Verdict
A once-useful workhorse now completely overshadowed by cheaper, more capable successors.
Quality score
31%
Pricing
$1.00/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
4k tokens
This is a pinned legacy snapshot (v0613) and may eventually be deprecated by OpenAI. The 4,095-token context window is its most significant practical limitation. OpenAI's own GPT-4o mini offers drastically more context and better quality at a comparable price — strongly consider migrating.
LegacyBudgetFastShort ContextOpenAI
Best for
High-volume, cost-sensitive text tasks like classification, summarization, and simple Q&A where bleeding-edge quality is not required.
GPT-3.5 Turbo Instruct is a legacy completion-style model from OpenAI, designed for instruction-following tasks using the older text completion API rather than the chat API. It excels at structured text generation, fill-in-the-middle tasks, and traditional NLP workflows that predate the chat paradigm.
Verdict
A legacy model only worth using if your pipeline depends on the text completion API.
Quality score
30%
Pricing
$1.50/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
4k tokens
Uses the legacy /v1/completions endpoint, not /v1/chat/completions. The 4,095-token context window is a hard constraint that makes it unsuitable for most modern tasks. OpenAI has not deprecated it, but it receives no capability updates.
LegacyCompletion APILow LatencyNarrow TasksOld Gen
Best for
Legacy completion API workflows, structured text generation, and simple instruction-following tasks where the chat format is not required.
GPT-5.6 Luna costs $0.2 per million input tokens and $1.2 per million output tokens on the API, with cached input at $0.02 per million. A month of 10M input and 2M output tokens runs about $4.40 at list price, before any batch or caching discounts.
What is the context window of GPT-5.6 Luna?
GPT-5.6 Luna has a 1.1M tokens context window, with up to 128k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
What is the knowledge cutoff of GPT-5.6 Luna?
GPT-5.6 Luna's training data runs through February 16, 2026, and the model was released on July 9, 2026. For anything after that date it needs web search or documents in the prompt.
What is GPT-5.6 Luna best for?
GPT-5.6 Luna is best for cheap high-throughput summarization, drafting, and routine agent steps. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid GPT-5.6 Luna?
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
What is a cheaper alternative to GPT-5.6 Luna?
GPT-5.1-Codex-Max (OpenAI) at $1.25/1M/1M input against GPT-5.6 Luna's $0.20/1M/1M. The strongest choice for serious software engineering work, provided you can absorb the output-side pricing. Compare it first if GPT-5.6 Luna's pricing is the thing stopping you.
What is a faster alternative to GPT-5.6 Luna?
GPT-3.5 Turbo — very fast against GPT-5.6 Luna's fast, with 16k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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