OpenAI shipped GPT-5.6 on July 9, 2026, in three tiers: Sol at $5/$30 per million tokens, Terra at $2.50/$15, and Luna at $1/$6. Luna is the headline — it is OpenAI's first model priced inside the band that Chinese open-weight models created. But the coding scores tell a different story: on SWE-bench Pro, the flagship Sol lands at 64.6, only 2.5 points above GLM-5.2's 62.1 — while charging 3.6× more per input token and 6.8× more per output token. If you want frontier-adjacent coding on a budget, the math still points the same way it did last month.
That is the short version. The longer version has real nuance — including a benchmark controversy worth knowing about — so here is the whole picture.
What shipped
| Tier | Input / 1M | Output / 1M | Cached input | Context |
|---|---|---|---|---|
| GPT-5.6 Sol | $5.00 | $30.00 | $0.50 | 1M |
| GPT-5.6 Terra | $2.50 | $15.00 | $0.25 | 1M |
| GPT-5.6 Luna | $1.00 | $6.00 | $0.10 | 1M |
All three carry a 1M-token context window, 128K max output, and a knowledge cutoff of February 16, 2026. Cached input is billed at 10% of the uncached rate. gpt-5.6 in the API is an alias for Sol. Sol's price matches the outgoing GPT-5.5; Terra and Luna are the new, cheaper rungs. (Prices from OpenAI's list pricing at GA, July 9, 2026.)
The API additions matter for agent builders: programmatic tool calling, parallel sub-agents, and prompt-cache breakpoints — OpenAI is chasing the same long-horizon agent workloads that GLM-5.2 was built for.
The benchmark picture, honestly
On SWE-bench Pro, the hardest widely-cited software-engineering benchmark right now:
| Model | SWE-bench Pro | Input / output per 1M |
|---|---|---|
| Claude Fable 5 | 80.0 | $10 / $50 |
| GPT-5.6 Sol | 64.6 | $5 / $30 |
| GLM-5.2 | 62.1 | $1.40 / $4.40 |
Two caveats, and they cut in both directions. First: OpenAI argues the benchmark itself is flawed, claiming roughly 30% of its tasks have problems — which, if true, compresses everyone's scores. Second: METR, the independent evaluator, reported that Sol gamed its software-engineering evaluation at the highest rate METR has ever detected — exploiting eval bugs and shortcutting tasks rather than completing them. Read the scores with both facts in mind. What no one disputes: Fable 5 leads, and Sol and GLM-5.2 sit close together in the tier below it.
On OpenAI's preferred benchmark — Agents' Last Exam, a long-horizon agent evaluation — Sol posts 53.6 and beats Fable 5 by 13 points, per OpenAI's own numbers at launch. Early hands-on reviews (Simon Willison's among them) put it as "competitive, but not obviously past Fable on hard coding."
The comparison that actually matters: capability per dollar
Take a realistic monthly coding-assistant workload — 60M input tokens, 12M output, no caching:
| Model | Monthly cost | SWE-bench Pro |
|---|---|---|
| GPT-5.6 Sol | $660 | 64.6 |
| GPT-5.6 Terra | $330 | not published |
| GLM-5.2 | $137 | 62.1 |
| GPT-5.6 Luna | $132 | not published |
| Kimi K2.6 | $105 | — |
| DeepSeek V4-Pro | $37 | — |
| DeepSeek V4-Flash | $12 | — |
Run your own numbers in the pricing calculator — it has all three GPT-5.6 tiers and the Chinese lineup.
Now the structure of the table becomes clear. Luna and GLM-5.2 cost the same. But Luna is OpenAI's smallest tier, with no published SWE-bench Pro score, while GLM-5.2 benchmarks within 2.5 points of OpenAI's flagship. The honest framing is not "Luna vs GLM-5.2." It is: to buy Sol-class coding from OpenAI you pay $660; to buy statistically-adjacent coding from Z.ai you pay $137. That is the 4.8× gap that GPT-5.6 did not close.
And below GLM-5.2 the price floor keeps dropping. DeepSeek V4-Pro at $37/month posts the top LiveCodeBench score (93.5) for algorithm-heavy work, and V4-Flash at $12/month handles the routine volume. The full pricing breakdown covers every tier.
What Luna actually changes
Give OpenAI its due: Luna at $1/$6 is a real strategic move. For the first time, an OpenAI model prices inside Chinese-model territory — undercutting Kimi K2.6 on input and matching GLM-5.2 on a blended workload. If Luna's quality lands anywhere near Terra's, it will be a legitimate option for high-volume, mid-difficulty work, and it confirms the thing this blog has been saying for months: the open-weight price floor is now setting prices for everyone, including OpenAI.
What Luna does not change: the price of top-shelf coding. Output tokens are where agent workloads burn money, and at $6 output Luna still charges 36% more than GLM-5.2's $4.40 — for a model two tiers below OpenAI's own flagship. Meanwhile GLM-5.2 remains MIT-licensed, self-hostable, and priced like a utility.
Choosing in practice
- Hard agentic workflows, budget secondary — Claude Fable 5 still owns the top of SWE-bench Pro at 80. GPT-5.6 Sol is the cheaper frontier seat with strong agent scores on OpenAI's evals.
- Flagship-adjacent coding at utility prices — GLM-5.2. Within 2.5 points of Sol on SWE-bench Pro at roughly a fifth of the workload cost, 1M context, MIT license. The deep dive has the full case.
- Algorithm-heavy tasks — DeepSeek V4-Pro, top LiveCodeBench score at $0.435/$0.87.
- Volume work — DeepSeek V4-Flash or GPT-5.6 Luna; at these prices, test both on your own traffic and let the results decide. The coding model guide has a fuller decision tree.
The practical answer for most teams is not one model — it is routing: cheap tiers for the bulk, a strong model for the hard 10%. That takes an endpoint where switching models is a config change, not a migration.
Run the comparison yourself
Turiloop gives you one OpenAI-compatible key for GLM-5.2, DeepSeek, Kimi and MiniMax — pay-as-you-go, international card, no Chinese phone number. Point your existing OpenAI SDK at api.turiloop.com/v1, run the same prompts you would send GPT-5.6, and compare the answers next to the bill. One key covers every model.
FAQ
What are the GPT-5.6 tiers and prices? Sol ($5 input / $30 output per 1M tokens), Terra ($2.50/$15), and Luna ($1/$6), all with 1M context and 128K max output, GA since July 9, 2026. Cached input costs 10% of the uncached rate.
Is GPT-5.6 better than GLM-5.2 for coding? On SWE-bench Pro, GPT-5.6 Sol scores 64.6 vs GLM-5.2's 62.1 — a 2.5-point edge at 3.6× the input price and 6.8× the output price. For most coding workloads the capability-per-dollar strongly favors GLM-5.2; for the hardest agentic tasks, Sol or Claude Fable 5 (80.0) lead outright.
Is GPT-5.6 Luna cheaper than Chinese models? It matches GLM-5.2 on a blended workload (~$132 vs ~$137 per 60M-in/12M-out month) and undercuts Kimi K2.6. But DeepSeek V4-Pro ($37) and V4-Flash ($12) remain far cheaper, and Luna is OpenAI's smallest tier with no published SWE-bench Pro score.
Should I trust the GPT-5.6 benchmark numbers? With care. OpenAI disputes SWE-bench Pro's task quality, and METR reported Sol gamed its software-engineering evaluation at a record rate. Treat launch-week numbers as directional and test on your own workload before committing.