gpt-5.6-luna
OpenAI · text, image → text
GPT-5.6 Luna is designed for cost-sensitive, high-volume workloads. It roughly corresponds to the nano model tier used in earlier GPT-5 families.
Input$0.20 /M
Output$1.20 /M
← Modelsgpt-5.6-luna
qwen3.8-max-preview
gpt-5.6-luna vs qwen3.8-max-preview
Output tokens cost $1.20 per million on gpt-5.6-luna and $1.01 per million on qwen3.8-max-preview. Input tokens cost $0.20 per million on gpt-5.6-luna and $0.34 per million on qwen3.8-max-preview. Cached input tokens are billed at $0.02 per million on gpt-5.6-luna and $0.03 per million on qwen3.8-max-preview. Context windows are 1,050,000 tokens on gpt-5.6-luna and 983,616 tokens on qwen3.8-max-preview. First-token latency measured on AIHubMix is 3.1s on gpt-5.6-luna and 2.4s on qwen3.8-max-preview. Measured output throughput is 93.6 tokens/s on gpt-5.6-luna and 48.1 tokens/s on qwen3.8-max-preview.
gpt-5.6-lunaqwen3.8-max-preview+ Add model 2/4
qwen3.8-max-preview
Qwen · text, image → text
Qwen 3.8 Max Preview(Qwen3.8-Max-Preview) is the latest-generation foundation model in the Qwen family, packing 2.4T parameters and still evolving. Compared with the previous flagship Qwen 3.7 Max, it delivers major gains in core capabilities like Coding and Cowork (professional productivity), with world-leading performance on complex, long-horizon tasks such as full-stack development, data analysis, and Office workflows. Launch offer: Credits are consumed at just 20% of the standard rate, effectively 5× your usage. Limited time only.
Input$0.34 /M
Output$1.01 /M
Specs & Pricing
Prices are per million tokens. Latency and throughput are rolling averages measured on AIHubMix.
gpt-5.6-luna
qwen3.8-max-preview
Input price
$0.20
$0.34
Output price
$1.20
$1.01
Cache read
$0.02
$0.03
Context window
1,050,000
983,616
Max output
128,000
131,072
Latency (first token)
3.1 s
2.4 s
Throughput
93.6 TPS
48.1 TPS
Modalities
textimage
textimage
Features
toolsthinkingstructured outputs
toolsfunction callingstructured outputsweblong contextthinking
Endpoints
chat_completions · responses
Promotional prices show the discounted rate; see each model page for promotion windows.
Usage on AIHubMix last 30 days
Daily traffic served through AIHubMix — how demand for each model is trending.
gpt-5.6-lunaqwen3.8-max-preview
Tokens / day
-
Requests / day
-
Live Performance last 3 days
Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.
gpt-5.6-lunaqwen3.8-max-preview
Throughput (TPS)
-
Latency (s)
-
Uptime (%)
-
Cost Calculator
Estimate your monthly bill for the same workload on each model.
gpt-5.6-luna
$30.00 /mo
qwen3.8-max-preview
$35.49 /mo
Monthly = daily × 30. Discounted rates applied where a promotion is active.
FAQ
Which is cheaper: gpt-5.6-luna, qwen3.8-max-preview?
qwen3.8-max-preview: $1.01/M output tokens; gpt-5.6-luna: $1.20/M. Use the cost calculator above to estimate your own workload.
Which responds faster?
qwen3.8-max-preview: 2.4s first-token latency measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
How large is each context window?
gpt-5.6-luna accepts 1,050,000 and qwen3.8-max-preview accepts 983,616 input tokens. Maximum output per request is 128,000 tokens on gpt-5.6-luna and 131,072 tokens on qwen3.8-max-preview.
Which one generates tokens faster?
gpt-5.6-luna at 93.6 tokens/s and qwen3.8-max-preview at 48.1 tokens/s, measured as output throughput on AIHubMix — a separate metric from first-token latency.
What inputs and capabilities does each model support?
gpt-5.6-luna accepts text and image input and supports tool calling, thinking and structured outputs; qwen3.8-max-preview accepts text and image input and supports tool calling, function calling, structured outputs, web search, long context and thinking.
Can I call gpt-5.6-luna and qwen3.8-max-preview with the same API key?
Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.
Popular Comparisons
Related model match-ups readers also look at.
AIHubMix