Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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. Choose North Mini Code if you need self-hosting or data privacy; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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 price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: North Mini Code ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-4o mini is API-metered at $0.15/$0.6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: North Mini Code holds 2× more — 256K (~384 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: North Mini Code is the newer model by about 23 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-4o mini
North Mini Code
Provider
OpenAI (US)
Cohere (Global)
Released
July 18, 2024
June 9, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
67.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier: GPT-4o mini — A core design strength of GPT-4o mini.
Strong coding for a small model (87.2% HumanEval): GPT-4o mini — A core design strength of GPT-4o mini.
Leading MMLU among peer small models (82%): GPT-4o mini — A core design strength of GPT-4o mini.
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.
Lowest cost at scale: North Mini Code — 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: North Mini Code — Its 256K window is about 2× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: North Mini Code — At Open weight (self-host / free) it undercuts GPT-4o mini, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: North Mini Code — Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs: North Mini Code — Open weights let you run it on your own hardware; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier: GPT-4o mini — 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-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. North Mini Code gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-4o mini 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-4o mini or North Mini Code better for coding?
Public SWE-Bench figures are not available for GPT-4o mini, so the honest test is your own repository — run an identical real bug through both. By design, GPT-4o mini leans toward very low cost per token for its capability tier while North Mini Code leans toward agentic software engineering, code generation, and terminal tasks, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or North Mini Code?
North Mini Code is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-4o mini is API-metered at $0.15/$0.6 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?
North Mini Code — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-4o mini and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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-4o mini or North Mini Code?
North Mini Code — released June 9, 2026, about 23 months after GPT-4o mini.
GPT-4o mini vs North Mini Code
OpenAI · US | Cohere · Global · Updated June 2026
Quick verdict
Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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. Choose North Mini Code if you need self-hosting or data privacy; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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 price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: North Mini Code ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-4o mini is API-metered at $0.15/$0.6 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: North Mini Code holds 2× more — 256K (~384 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: North Mini Code is the newer model by about 23 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-4o mini
North Mini Code
Provider
OpenAI (US)
Cohere (Global)
Released
July 18, 2024
June 9, 2026
Context window
128K (~192 pages)
256K (~384 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
67.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier
GPT-4o mini
A core design strength of GPT-4o mini.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
A core design strength of GPT-4o mini.
Leading MMLU among peer small models (82%)
GPT-4o mini
A core design strength of GPT-4o mini.
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.
Lowest cost at scale
North Mini Code
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
North Mini Code
Its 256K window is about 2× larger, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ North Mini Code
At Open weight (self-host / free) it undercuts GPT-4o mini, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ North Mini Code
Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ North Mini Code
Open weights let you run it on your own hardware; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier
→ GPT-4o mini
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-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. North Mini Code gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-4o mini 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-4o mini 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-4o mini or North Mini Code better for coding?
Public SWE-Bench figures are not available for GPT-4o mini, so the honest test is your own repository — run an identical real bug through both. By design, GPT-4o mini leans toward very low cost per token for its capability tier while North Mini Code leans toward agentic software engineering, code generation, and terminal tasks, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or North Mini Code?
North Mini Code is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-4o mini is API-metered at $0.15/$0.6 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?
North Mini Code — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-4o mini and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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-4o mini or North Mini Code?
North Mini Code — released June 9, 2026, about 23 months after GPT-4o mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.