Qwen 3.8 Flash wins on price ($0.16 vs $0.2/1M input). GPT-5.6 Luna wins on coding (88 vs 84) and writing quality. For most workflows, GPT-5.6 Luna is the stronger default — best budget model from a frontier lab — near-frontier scores at commodity price.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GPT-5.6 Luna is the safest overall answer here when you want the strongest default instead of the lowest list price.
Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.
Qwen 3.8 Flash is the better pick when response speed matters more than maximum reasoning depth.
GPT-5.6 Luna leads on coding with a score of 88 vs 84 for Qwen 3.8 Flash.
GPT-5.6 Luna has the larger context window: 1.05M vs 991K for Qwen 3.8 Flash.
Qwen 3.8 Flash is cheaper at $0.16/1M input tokens vs $0.2/1M for GPT-5.6 Luna.
GPT-5.6 Luna is the safer default: it is built for cheap high-throughput summarization, drafting, and routine agent steps, which covers most of what people bring to this comparison.
Choose Qwen 3.8 Flash when your work is mostly cheap high-throughput coding and reasoning — that is the workload it was tuned for.
Qwen 3.8 Flash is the more cost-efficient option at $0.16/1M input — GPT-5.6 Luna costs 1x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
OpenAI / Budget / Aug 6, 2026
Best budget model from a frontier lab — near-frontier scores at commodity price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
The fastest way to see where the recommendation shifts when your priority changes.
SWE-bench Pro 62.5 at sixteen cents per million input.
Best budget model from a frontier lab — near-frontier scores at commodity price.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-5.6 LunaOpenAI | $0.20/1M | $1.20/1M | $4.40 | 1.1M tokens | Fast | 88 | 85 | 84 |
| Qwen 3.8 FlashAlibaba | $0.16/1M | $0.47/1M | $2.54 | 991k tokens | Very fast | 84 | 76 | 78 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
The small, fast, cheap tier of the GPT-5.6 family — near-frontier scores on many benchmarks at commodity pricing after its ~80% July price cut.
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
The budget disruptor of 2026. After the price cut, Luna delivers benchmark scores that embarrass models 10x its price. Just don't trust it with genuinely long context — recall collapses past 512K tokens.
Fully public July 9, 2026; price cut ~80% to $0.20/$1.20 on July 30, 2026 (launched at $1/$6). Many third-party pages still show the old price.
Alibaba's preview of the Qwen4 architecture — 125B parameters with only 6B active per token, at sixteen cents per million input.
You need Alibaba's maximum capability — that is Qwen 3.8 Max — or a SWE-bench Verified number.
One of the best coding-score-per-dollar picks in the catalog. Route volume work here and reserve Qwen 3.8 Max or a frontier model for the hard cases.
Released August 26, 2026. The open-weight release is Qwen3.8-Flash-Next, a preview of the Qwen4 architecture: 125B mixture-of-experts with 6B active per token, a 51B n-gram embedding table and a 4B multi-token prediction layer. Qwen 3.8 Flash is the production API version on Qwen Cloud at $0.16/$0.47.
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GPT-5.6 Luna wins on more of the categories we score — coding, writing, budget — so it is the better default of the two. Qwen 3.8 Flash is the better pick when your work is mostly cheap high-throughput coding and reasoning. Neither is universally "better": GPT-5.6 Luna is aimed at cheap high-throughput summarization and drafting, Qwen 3.8 Flash at cheap high-throughput coding and reasoning.
Qwen 3.8 Flash is cheaper at $0.16/1M input and $0.47/1M output. GPT-5.6 Luna costs $0.2/1M input and $1.2/1M output.
GPT-5.6 Luna has the larger context window at 1.05M tokens vs Qwen 3.8 Flash's 991K. For large document analysis, GPT-5.6 Luna is the stronger pick.
GPT-5.6 Luna is better for coding with a score of 88 vs Qwen 3.8 Flash's 84 (out of 100). Claude Fable 5 is the overall coding leader in this directory at 100/100.
Qwen 3.8 Flash is faster with a very fast speed rating (score: 5) vs GPT-5.6 Luna's fast rating (score: 4). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-5.6 Luna latency penalty is usually invisible.
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. Avoid it if your workload actually uses the long context window — recall drops to 41% past 512K tokens. That is the main case for looking at Qwen 3.8 Flash instead.
No published SWE-bench Verified score, only SWE-bench Pro. An architecture preview rather than a settled flagship — Qwen 3.8 Max remains Alibaba's top-end model. Avoid it if you need Alibaba's maximum capability — that is Qwen 3.8 Max — or a SWE-bench Verified number. Against GPT-5.6 Luna specifically, the gap shows up most on coding (88 vs 84).
Take a moderate workload of 10M input and 2M output tokens a month. Qwen 3.8 Flash runs $2.54 (at $0.16/1M in and $0.47/1M out); GPT-5.6 Luna runs $4.40 (at $0.2/1M in and $1.2/1M out). That is a $1.86/month difference — Qwen 3.8 Flash is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-5.6 Luna for cheap high-throughput summarization and drafting, with Qwen 3.8 Flash handling cheap high-throughput coding and reasoning. Routing high-volume, low-stakes calls to Qwen 3.8 Flash at $0.16/1M and reserving GPT-5.6 Luna for the hard cases is usually the cheapest arrangement that does not cost you quality.