Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Pick Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7) or 1m-token long-document and full-codebase analysis. On a tight budget at scale, GPT-6 Luna is the value pick.
GPT-6 Luna (OpenAI, US) and Qwen 3.7 Max (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Qwen 3.7 Max is alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: GPT-6 Luna is about 25× cheaper on input ($0.1/$0.5 per 1M tokens vs $2.5/$7.5 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: 1.05M tokens vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: GPT-6 Luna is the newer model by about 4 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GPT-6 Luna
Qwen 3.7 Max
Provider
OpenAI (US)
Alibaba (China)
Released
September 22, 2026
May 20, 2026
Context window
1.05M tokens (~1,575 pages)
1M (~1,500 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$2.5/$7.5 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — At $0.1/$0.5 per 1M tokens it undercuts Qwen 3.7 Max ($2.5/$7.5 per 1M tokens), and that gap compounds at volume.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it runs cheaper at $0.1/$0.5 per 1M tokens.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7): Qwen 3.7 Max — Qwen 3.7 Max lists long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7) among its strengths; GPT-6 Luna does not.
1M-token long-document and full-codebase analysis: Qwen 3.7 Max — Qwen 3.7 Max lists 1M-token long-document and full-codebase analysis among its strengths; GPT-6 Luna does not.
MCP tool orchestration and multi-hour autonomous runs: Qwen 3.7 Max — Qwen 3.7 Max lists mCP tool orchestration and multi-hour autonomous runs among its strengths; GPT-6 Luna does not.
Lowest cost at scale: GPT-6 Luna — At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-6 Luna — At $0.1/$0.5 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-6 Luna — Larger 1.05M tokens window fits more in one prompt.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — It is specifically built for that.
Anyone whose priority is long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7): Qwen 3.7 Max — That is its strongest area.
An enterprise with regional data-residency rules: GPT-6 Luna or Qwen 3.7 Max — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
Qwen 3.7 Max: where it fits
Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. Released May 20, 2026 by Alibaba, it is built for long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7), 1M-token long-document and full-codebase analysis, mCP tool orchestration and multi-hour autonomous runs, and frontier intelligence at roughly half the price of US flagships.
Its trade-offs: text-only — no vision input (the Plus variant adds images), closed-weight, API-only — no self-hosting, trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning, and chinese-jurisdiction data-residency considerations. At $2.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GPT-6 Luna (US) and Qwen 3.7 Max (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. GPT-6 Luna is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is GPT-6 Luna or Qwen 3.7 Max better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume while Qwen 3.7 Max leans toward long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6 Luna or Qwen 3.7 Max?
GPT-6 Luna is cheaper — $0.1/$0.5 per 1M tokens vs $2.5/$7.5 per 1M tokens, roughly 25× apart on input.
Which has the bigger context window?
Effectively neither — 1.05M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-6 Luna and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, Qwen 3.7 Max and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GPT-6 Luna or Qwen 3.7 Max?
GPT-6 Luna — released September 22, 2026, about 4 months after Qwen 3.7 Max.
GPT-6 Luna vs Qwen 3.7 Max
OpenAI · US | Alibaba · China · Updated June 2026
Quick verdict
Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Pick Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7) or 1m-token long-document and full-codebase analysis. On a tight budget at scale, GPT-6 Luna is the value pick.
GPT-6 Luna (OpenAI, US) and Qwen 3.7 Max (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Qwen 3.7 Max is alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GPT-6 Luna is about 25× cheaper on input ($0.1/$0.5 per 1M tokens vs $2.5/$7.5 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: 1.05M tokens vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: GPT-6 Luna is the newer model by about 4 months (released September 22, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GPT-6 Luna
Qwen 3.7 Max
Provider
OpenAI (US)
Alibaba (China)
Released
September 22, 2026
May 20, 2026
Context window
1.05M tokens (~1,575 pages)
1M (~1,500 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$2.5/$7.5 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
GPT-6 Luna
At $0.1/$0.5 per 1M tokens it undercuts Qwen 3.7 Max ($2.5/$7.5 per 1M tokens), and that gap compounds at volume.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it runs cheaper at $0.1/$0.5 per 1M tokens.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7)
Qwen 3.7 Max
Qwen 3.7 Max lists long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7) among its strengths; GPT-6 Luna does not.
1M-token long-document and full-codebase analysis
Qwen 3.7 Max
Qwen 3.7 Max lists 1M-token long-document and full-codebase analysis among its strengths; GPT-6 Luna does not.
MCP tool orchestration and multi-hour autonomous runs
Qwen 3.7 Max
Qwen 3.7 Max lists mCP tool orchestration and multi-hour autonomous runs among its strengths; GPT-6 Luna does not.
Lowest cost at scale
GPT-6 Luna
At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-6 Luna
At $0.1/$0.5 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Luna
Larger 1.05M tokens window fits more in one prompt.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
→ GPT-6 Luna
It is specifically built for that.
Anyone whose priority is long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7)
→ Qwen 3.7 Max
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-6 Luna or Qwen 3.7 Max
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
Qwen 3.7 Max: where it fits
Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. Released May 20, 2026 by Alibaba, it is built for long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7), 1M-token long-document and full-codebase analysis, mCP tool orchestration and multi-hour autonomous runs, and frontier intelligence at roughly half the price of US flagships.
Its trade-offs: text-only — no vision input (the Plus variant adds images), closed-weight, API-only — no self-hosting, trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning, and chinese-jurisdiction data-residency considerations. At $2.5 in / $7.5 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." GPT-6 Luna (US) and Qwen 3.7 Max (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. GPT-6 Luna is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both GPT-6 Luna and Qwen 3.7 Max without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume while Qwen 3.7 Max leans toward long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6 Luna or Qwen 3.7 Max?
GPT-6 Luna is cheaper — $0.1/$0.5 per 1M tokens vs $2.5/$7.5 per 1M tokens, roughly 25× apart on input.
Which has the bigger context window?
Effectively neither — 1.05M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-6 Luna and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, Qwen 3.7 Max and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GPT-6 Luna or Qwen 3.7 Max?
GPT-6 Luna — released September 22, 2026, about 4 months after Qwen 3.7 Max.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.