Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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. Choose gpt-oss-120b if you need self-hosting or data privacy; Qwen 3.7 Max if you want a managed API.
gpt-oss-120b (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-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: gpt-oss-120b ships open weights you can self-host (hardware cost only, no per-token fee), while Qwen 3.7 Max is API-metered at $2.5/$7.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Qwen 3.7 Max holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Qwen 3.7 Max is the newer model by about 10 months (released May 20, 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-oss-120b
Qwen 3.7 Max
Provider
OpenAI (US)
Alibaba (China)
Released
August 5, 2025
May 20, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$2.5/$7.5 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4: gpt-oss-120b — A core design strength of gpt-oss-120b.
Configurable reasoning depth (low/medium/high): gpt-oss-120b — A core design strength of gpt-oss-120b.
Agentic tool use, function calling, and code execution: gpt-oss-120b — A core design strength of gpt-oss-120b.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7): Qwen 3.7 Max — A core design strength of Qwen 3.7 Max.
1M-token long-document and full-codebase analysis: Qwen 3.7 Max — A core design strength of Qwen 3.7 Max.
MCP tool orchestration and multi-hour autonomous runs: Qwen 3.7 Max — A core design strength of Qwen 3.7 Max.
Lowest cost at scale: gpt-oss-120b — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Qwen 3.7 Max — Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: gpt-oss-120b — At Open weight (self-host / free) it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen 3.7 Max — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: gpt-oss-120b — Open weights let you run it on your own hardware; Qwen 3.7 Max is API-only.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4: gpt-oss-120b — 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-oss-120b 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-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
The defining split here is open vs. closed. gpt-oss-120b gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Qwen 3.7 Max gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is gpt-oss-120b or Qwen 3.7 Max better for coding?
Public SWE-Bench figures are not available for Qwen 3.7 Max, so the honest test is your own repository — run an identical real bug through both. By design, gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4 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-oss-120b or Qwen 3.7 Max?
gpt-oss-120b is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.7 Max is API-metered at $2.5/$7.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Qwen 3.7 Max — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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-oss-120b or Qwen 3.7 Max?
Qwen 3.7 Max — released May 20, 2026, about 10 months after gpt-oss-120b.
gpt-oss-120b vs Qwen 3.7 Max
OpenAI · US | Alibaba · China · Updated June 2026
Quick verdict
Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). 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. Choose gpt-oss-120b if you need self-hosting or data privacy; Qwen 3.7 Max if you want a managed API.
gpt-oss-120b (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-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: gpt-oss-120b ships open weights you can self-host (hardware cost only, no per-token fee), while Qwen 3.7 Max is API-metered at $2.5/$7.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Qwen 3.7 Max holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Qwen 3.7 Max is the newer model by about 10 months (released May 20, 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-oss-120b
Qwen 3.7 Max
Provider
OpenAI (US)
Alibaba (China)
Released
August 5, 2025
May 20, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
Open weight (self-host / free)
$2.5/$7.5 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
A core design strength of gpt-oss-120b.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
A core design strength of gpt-oss-120b.
Agentic tool use, function calling, and code execution
gpt-oss-120b
A core design strength of gpt-oss-120b.
Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7)
Qwen 3.7 Max
A core design strength of Qwen 3.7 Max.
1M-token long-document and full-codebase analysis
Qwen 3.7 Max
A core design strength of Qwen 3.7 Max.
MCP tool orchestration and multi-hour autonomous runs
Qwen 3.7 Max
A core design strength of Qwen 3.7 Max.
Lowest cost at scale
gpt-oss-120b
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Qwen 3.7 Max
Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ gpt-oss-120b
At Open weight (self-host / free) it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen 3.7 Max
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ gpt-oss-120b
Open weights let you run it on your own hardware; Qwen 3.7 Max is API-only.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
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-oss-120b 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-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs are real: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
The defining split here is open vs. closed. gpt-oss-120b gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Qwen 3.7 Max gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both gpt-oss-120b 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.
Is gpt-oss-120b or Qwen 3.7 Max better for coding?
Public SWE-Bench figures are not available for Qwen 3.7 Max, so the honest test is your own repository — run an identical real bug through both. By design, gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4 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-oss-120b or Qwen 3.7 Max?
gpt-oss-120b is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.7 Max is API-metered at $2.5/$7.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Qwen 3.7 Max — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and Qwen 3.7 Max together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, 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-oss-120b or Qwen 3.7 Max?
Qwen 3.7 Max — released May 20, 2026, about 10 months after gpt-oss-120b.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.