gpt-oss-120b vs MiniMax M2.7
OpenAI · US | MiniMax · 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 MiniMax M2.7 for agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported) or independently ranked 14th of 97 on the artificial analysis intelligence index. On a tight budget at scale, gpt-oss-120b is the value pick.
gpt-oss-120b (OpenAI, US) and MiniMax M2.7 (MiniMax, 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. MiniMax M2.7 is a cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: MiniMax M2.7 holds 1.6× more — 205K (~307 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: MiniMax M2.7 is the newer model by about 8 months (released March 18, 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 | MiniMax M2.7 |
|---|---|---|
| Provider | OpenAI (US) | MiniMax (China) |
| Released | August 5, 2025 | March 18, 2026 |
| Context window | 131K (~197 pages) | 205K (~307 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.3/$1.2 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| 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
gpt-oss-120b lists self-hostable on a single 80GB H100 GPU via MXFP4 among its strengths; MiniMax M2.7 does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; MiniMax M2.7 does not.
Agentic tool use, function calling, and code execution
gpt-oss-120b
gpt-oss-120b lists agentic tool use, function calling, and code execution among its strengths; MiniMax M2.7 does not.
Agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported)
MiniMax M2.7
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it carries the larger 205K context.
Independently ranked 14th of 97 on the Artificial Analysis Intelligence Index
MiniMax M2.7
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks — and it is the newer of the two.
Sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware
MiniMax M2.7
gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed models
Lowest cost at scale
gpt-oss-120b
Its weights are open, so at volume you pay for your own hardware instead of MiniMax M2.7's $0.3/$1.2 per 1M tokens.
Largest single-prompt input
MiniMax M2.7
Its 205K window is about 1.6× larger than gpt-oss-120b's 131K, fitting roughly 307 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 MiniMax M2.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M2.7
Larger 205K window fits more in one prompt.
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 agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported)
→ MiniMax M2.7
That is its strongest area.
An enterprise with regional data-residency rules
→ gpt-oss-120b or MiniMax M2.7
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.
MiniMax M2.7: where it fits
A cheap open-weight agentic coder with near-frontier terminal scores — held back by a non-commercial licence and non-standard benchmarks. Released March 18, 2026 by MiniMax, it is built for agentic and terminal coding well above its price tier (57.0 on Terminal-Bench 2, vendor-reported), independently ranked 14th of 97 on the Artificial Analysis Intelligence Index, sparse mixture-of-experts — roughly 230B total but only ~10B active, so it runs on local hardware, and served by five separate hosts at uniform pricing, so there is no provider lock-in.
Its trade-offs: open weights but a NON-COMMERCIAL licence — commercial use requires prior written authorisation from MiniMax, and at least one major tracker still mislabels it as MIT, reports SWE-Bench Pro instead of the standard Verified set, which blocks like-for-like comparison, and already superseded internally by M3, and its 205K context is small against 1M-class rivals. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." gpt-oss-120b (US) and MiniMax M2.7 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. gpt-oss-120b 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-oss-120b and MiniMax M2.7 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.
See pricingFrequently asked questions
Is gpt-oss-120b or MiniMax M2.7 better for coding?
Public SWE-Bench figures are not available for MiniMax M2.7, 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 MiniMax M2.7 leans toward agentic and terminal coding well above its price tier (57.0 on terminal-bench 2, vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, gpt-oss-120b or MiniMax M2.7?
gpt-oss-120b is cheaper — Open weight (self-host / free) vs $0.3/$1.2 per 1M tokens.
Which has the bigger context window?
MiniMax M2.7 — 205K vs 131K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and MiniMax M2.7 together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, MiniMax M2.7 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 MiniMax M2.7?
MiniMax M2.7 — released March 18, 2026, about 8 months after gpt-oss-120b.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.