My commentary about the model at the bottom of the post.
For some time now I have been measuring GPT model's allowances and several others. Because a new GPT model was released, it is time for an update.
Unfortunately, the trend continues. With each new model release, the new model gets less quota in API-equivalent compared to the previous ones. Also, unfortunately, the claim that we get 2x the usage out of Terra compared to Sol is a straight-up lie.
Measured on a fresh context window on a Plus account for accurate measurements, like always. Context limited to 272k to make sure I don't go past the double price. I didn't check Luna or Astra yet, but Sol remains as it was. Generally the allowance for models is constant after their release from my measurements; only the new ones have less.
| Name |
Per 5 hours |
Per week |
Per month (30 days) |
API cost÷Sub price ($20) ratio |
| GPT-5.6 Sol |
$14.4 |
$86.4 |
$370 |
18.5 |
| GPT 6 Luna |
$6.5 |
$39 |
$167 |
8.35 |
| GPT-6 Sol |
$11 |
$66 |
$283 |
14.15 |
| GPT-6.1 Terra |
$5.33 |
$32 |
$137 |
6.85 |
| GPT-6.1 Sol |
$7.8 |
$46.8 |
$200 |
10 |
| GPT-6 Astra |
$10 |
$60 |
$257 |
12.85 |
For Pro, simply multiply by 5/10/25.
As a bonus, here is a table of the "real cost" per million tokens and total amount of tokens per real dollar spent, also compared to DeepSeek v4.1 Flash (off-peak pricing), GLM 5.3 (off-peak), and Muse Spark. If you use OpenCode, Ollama Cloud, or CommandCode subscriptions, the price for DeepSeek goes down. At peak hours, the price for DeepSeek and GLM is 2x. Ollama has its own infrastructure with ZDR and uses different peak hours.
For z.ai plans, you can subscribe quarterly or yearly for better ratings.
| Model |
Input |
Cached Input |
Output |
Tokens per $1 |
| GPT-5.6 Sol ÷ 18.5 |
$0.216216 |
$0.021622 |
$1.081081 |
31.46M |
| GPT-6 Luna ÷ 8.35 |
$0.011976 |
$0.001198 |
$0.059880 |
567.90M |
| GPT-6 Sol ÷ 14.15 |
$0.141343 |
$0.014134 |
$0.706714 |
48.13M |
| GPT-6.1 Terra ÷ 6.85 |
$0.145985 |
$0.007299 |
$0.729927 |
69.54M |
| GPT-6.1 Sol ÷ 10 |
$0.200000 |
$0.010000 |
$1.000000 |
50.76M |
| GPT-6 Astra ÷ 12.85 |
$0.778210 |
$0.077821 |
$3.891051 |
8.74M |
| Claude Opus 5.5 ÷ 42* |
$0.095238 |
$0.004762 |
$0.476190 |
106.60M |
| DeepSeek V4.1 Flash (API) |
$0.150000 |
$0.003000 |
$0.600000 |
103.52M |
| DeepSeek V4.1 Flash (Ollama) ÷ 3 |
$0.050000 |
$0.001000 |
$0.200000 |
310.56M |
| DeepSeek V4.1 Flash (CommandCode/OpenCode) ÷ 8 |
$0.018750 |
$0.000375 |
$0.075000 |
828.16M |
| Muse Spark 1.3 Contributor (OpenCode) ÷ 6 |
$0.016667 |
$0.000333 |
$0.033333 |
1,103.33M |
| GLM-5.3 (Ollama/OpenCode $40 Plan) ÷ 3 |
$0.466667 |
$0.086667 |
$1.466667 |
9.70M |
| GLM-5.3 (Z.ai Lite) ÷ 7.668 |
$0.182577 |
$0.033907 |
$0.573813 |
24.80M |
| GLM-5.3 (Z.ai Pro) ÷ 10.352 |
$0.135240 |
$0.025116 |
$0.425039 |
33.48M |
| GLM-5.3 (Z.ai Max) ÷ 11.502 |
$0.121718 |
$0.022605 |
$0.382542 |
37.20M |
| GLM-5.3 Flash (Z.ai Lite) ÷ 2.657 |
$0.056455 |
$0.011291 |
$0.188182 |
75.16M |
| GLM-5.3 Flash (Z.ai Pro) ÷ 3.586 |
$0.041829 |
$0.008366 |
$0.139431 |
101.44M |
| GLM-5.3 Flash (Z.ai Max) ÷ 3.985 |
$0.037641 |
$0.007528 |
$0.125471 |
112.73M |
| GLM-5.3 Flash (OpenCode) ÷ 6 |
$0.025000 |
$0.005000 |
$0.083333 |
169.73M |
| GLM-5.3 Flash (CommandCode) ÷ 8 |
$0.018750 |
$0.003750 |
$0.062500 |
226.31M |
Tokens per $1 assumes 97% cached input, 2.5% uncached input, and 0.5% output.
* - Claude charges for cache writes. The pricing is averaged. I used these numbers. Their findings for Codex match mine, so I trust them.
My commentary about the new model. It is the first and only model that simply doesn't want to work. During a simple mechanical refactor, it stopped 3 times, telling me it is only finished partially. At the end it changed a part of the code, ran tests, saw several errors, reverted the change, and reported that it is only partially done. I ran it on XHigh reasoning. I doubt Max would change much, but it is not out of the question. This is not a model for agentic coding, that's for sure. It is quick, but very unreliable.
I double-checked 6.1 Sol allowance; it is still the same as it was, so I recommend staying with 6.1 Sol or Luna. I have no use for 6.1 Terra. I see no reason in using it for coding when it gives us almost the same usage as 6.1 Sol. If you don't want your job done, use the new Terra, I guess. It may have a potential as an orchestrator, but that requires further testing.
Unfortunately, OpenAI continues its tactic to nerf the usage with each new model. By my own measurements, they don't nerf the usage of each model after they are released; they are nerfed from the release (compared to previous ones). You can see the trend with 5.6 Sol -> 6 Sol - 6.1 Sol -> 6.1 Terra. We are at the third of the 5.6 allowance now, which was also lowered compared to 5.5.