Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). Pick North Mini Code for agentic software engineering, code generation, and terminal tasks or efficient sparse moe — 3b active of 30b, runs on a single h100.
gpt-oss-120b (OpenAI) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. 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. North Mini Code is cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. They diverge most on context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Context window: North Mini Code holds 2× more — 256K (~384 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.
Coding: North Mini Code leads SWE-Bench Verified by 5.2 points (62.4% vs 67.6%) — a real edge on hard, real-world software tasks.
Recency: North Mini Code is the newer model by about 10 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
gpt-oss-120b
North Mini Code
Provider
OpenAI (US)
Cohere (Global)
Released
August 5, 2025
June 9, 2026
Context window
131K (~197 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
67.6%
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.
Agentic software engineering, code generation, and terminal tasks: North Mini Code — A core design strength of North Mini Code.
Efficient sparse MoE — 3B active of 30B, runs on a single H100: North Mini Code — A core design strength of North Mini Code.
High throughput (up to 2.8x Devstral Small 2) at low latency: North Mini Code — A core design strength of North Mini Code.
Largest single-prompt input: North Mini Code — Its 256K window is about 2× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: North Mini Code — Larger 256K 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 software engineering, code generation, and terminal tasks: North Mini Code — That is its strongest area.
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.
North Mini Code: where it fits
Cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. Released June 9, 2026 by Cohere, it is built for agentic software engineering, code generation, and terminal tasks, efficient sparse MoE — 3B active of 30B, runs on a single H100, high throughput (up to 2.8x Devstral Small 2) at low latency, and fully open weights under Apache 2.0 with fp8 and 4-bit builds.
Its trade-offs: text-only and coding-specialized — not multimodal or general-purpose, and 256K context and modest general-intelligence index trail frontier models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
gpt-oss-120b and North Mini Code overlap enough that the right pick depends on your specific job. North Mini Code holds the larger context; and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, North Mini Code for agentic software engineering, code generation, and terminal tasks. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is gpt-oss-120b or North Mini Code better for coding?
On SWE-Bench Verified, gpt-oss-120b scores 62.4% and North Mini Code scores 67.6% — North Mini Code has the measurable edge.
Which is cheaper, gpt-oss-120b or North Mini Code?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
North Mini Code — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, North Mini Code 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 North Mini Code?
North Mini Code — released June 9, 2026, about 10 months after gpt-oss-120b.
gpt-oss-120b vs North Mini Code
OpenAI · US | Cohere · Global · 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 North Mini Code for agentic software engineering, code generation, and terminal tasks or efficient sparse moe — 3b active of 30b, runs on a single h100.
gpt-oss-120b (OpenAI) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. 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. North Mini Code is cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. They diverge most on context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: North Mini Code holds 2× more — 256K (~384 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.
▸Coding: North Mini Code leads SWE-Bench Verified by 5.2 points (62.4% vs 67.6%) — a real edge on hard, real-world software tasks.
▸Recency: North Mini Code is the newer model by about 10 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
gpt-oss-120b
North Mini Code
Provider
OpenAI (US)
Cohere (Global)
Released
August 5, 2025
June 9, 2026
Context window
131K (~197 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
62.4%
67.6%
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.
Agentic software engineering, code generation, and terminal tasks
North Mini Code
A core design strength of North Mini Code.
Efficient sparse MoE — 3B active of 30B, runs on a single H100
North Mini Code
A core design strength of North Mini Code.
High throughput (up to 2.8x Devstral Small 2) at low latency
North Mini Code
A core design strength of North Mini Code.
Largest single-prompt input
North Mini Code
Its 256K window is about 2× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ North Mini Code
Larger 256K 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 software engineering, code generation, and terminal tasks
→ North Mini Code
That is its strongest area.
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.
North Mini Code: where it fits
Cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. Released June 9, 2026 by Cohere, it is built for agentic software engineering, code generation, and terminal tasks, efficient sparse MoE — 3B active of 30B, runs on a single H100, high throughput (up to 2.8x Devstral Small 2) at low latency, and fully open weights under Apache 2.0 with fp8 and 4-bit builds.
Its trade-offs: text-only and coding-specialized — not multimodal or general-purpose, and 256K context and modest general-intelligence index trail frontier models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
gpt-oss-120b and North Mini Code overlap enough that the right pick depends on your specific job. North Mini Code holds the larger context; and each leads in its own area — gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4, North Mini Code for agentic software engineering, code generation, and terminal tasks. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both gpt-oss-120b and North Mini Code 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 North Mini Code better for coding?
On SWE-Bench Verified, gpt-oss-120b scores 62.4% and North Mini Code scores 67.6% — North Mini Code has the measurable edge.
Which is cheaper, gpt-oss-120b or North Mini Code?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
North Mini Code — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both gpt-oss-120b and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you gpt-oss-120b, North Mini Code 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 North Mini Code?
North Mini Code — released June 9, 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.