Both are OpenAI models. gpt-oss-120b is the newer, generally stronger default; reach for GPT-4o mini when a specific cost or latency profile matters more than the latest capabilities.
GPT-4o mini and gpt-oss-120b are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. 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. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Cost model: gpt-oss-120b 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: gpt-oss-120b holds 1× more — 131K (~197 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: gpt-oss-120b is the newer model by about 13 months (released August 5, 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-4o mini
gpt-oss-120b
Provider
OpenAI (US)
OpenAI (US)
Released
July 18, 2024
August 5, 2025
Context window
128K (~192 pages)
131K (~197 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
62.4%
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.
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.
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: gpt-oss-120b — Its 131K window is about 1× larger, fitting roughly 197 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 GPT-4o mini, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: gpt-oss-120b — Larger 131K 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; 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 self-hostable on a single 80gb h100 gpu via mxfp4: gpt-oss-120b — 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.
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: 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.
The bottom line for this matchup
Because GPT-4o mini and gpt-oss-120b come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. gpt-oss-120b is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to gpt-oss-120b and drop down only with a concrete reason.
Frequently asked questions
Is GPT-4o mini or gpt-oss-120b 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 gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or gpt-oss-120b?
gpt-oss-120b 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?
gpt-oss-120b — 131K vs 128K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GPT-4o mini to gpt-oss-120b?
Since both are OpenAI models, the newer one (gpt-oss-120b) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-4o mini or gpt-oss-120b?
gpt-oss-120b — released August 5, 2025, about 13 months after GPT-4o mini.
GPT-4o mini vs gpt-oss-120b
OpenAI · US | OpenAI · US · Updated June 2026
Quick verdict
Both are OpenAI models. gpt-oss-120b is the newer, generally stronger default; reach for GPT-4o mini when a specific cost or latency profile matters more than the latest capabilities.
GPT-4o mini and gpt-oss-120b are both OpenAI models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. 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. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
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 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: gpt-oss-120b holds 1× more — 131K (~197 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: gpt-oss-120b is the newer model by about 13 months (released August 5, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-4o mini
gpt-oss-120b
Provider
OpenAI (US)
OpenAI (US)
Released
July 18, 2024
August 5, 2025
Context window
128K (~192 pages)
131K (~197 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
62.4%
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.
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.
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
gpt-oss-120b
Its 131K window is about 1× larger, fitting roughly 197 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 GPT-4o mini, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ gpt-oss-120b
Larger 131K 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; 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 self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
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.
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: 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.
The bottom line for this matchup
Because GPT-4o mini and gpt-oss-120b come from the same lab (OpenAI), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. gpt-oss-120b is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to gpt-oss-120b and drop down only with a concrete reason.
Want both GPT-4o mini and gpt-oss-120b 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 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 gpt-oss-120b leans toward self-hostable on a single 80gb h100 gpu via mxfp4, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-4o mini or gpt-oss-120b?
gpt-oss-120b 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?
gpt-oss-120b — 131K vs 128K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from GPT-4o mini to gpt-oss-120b?
Since both are OpenAI models, the newer one (gpt-oss-120b) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, GPT-4o mini or gpt-oss-120b?
gpt-oss-120b — released August 5, 2025, about 13 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.