GPT-4o mini vs MiniMax M3.1 Flash

OpenAI · US  |  MiniMax · China · 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 MiniMax M3.1 Flash for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length or 428b-parameter moe (about 23b active) with weights on hugging face from october 23, 2026. Choose MiniMax M3.1 Flash if you need self-hosting or data privacy; GPT-4o mini if you want a managed API.

GPT-4o mini (OpenAI, US) and MiniMax M3.1 Flash (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-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. MiniMax M3.1 Flash is miniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGPT-4o miniMiniMax M3.1 Flash
ProviderOpenAI (US) MiniMax (China)
ReleasedJuly 18, 2024 October 19, 2026
Context window128K (~192 pages) 1M tokens (~1,500 pages)
Price (in/out)$0.15/$0.6 per 1M tokens $0.15/$0.6 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, image, video, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Very low cost per token for its capability tier

GPT-4o mini

MiniMax M3.1 Flash is comparatively weak here — thinking is always on and default effort is max, which inflates output-token cost

Strong coding for a small model (87.2% HumanEval)

GPT-4o mini

GPT-4o mini lists strong coding for a small model (87.2% HumanEval) among its strengths; MiniMax M3.1 Flash does not.

Leading MMLU among peer small models (82%)

GPT-4o mini

GPT-4o mini lists leading MMLU among peer small models (82%) among its strengths; MiniMax M3.1 Flash does not.

$0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

MiniMax M3.1 Flash

Its 1M tokens window holds about 7.8× more than GPT-4o mini's 128K in a single prompt.

428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026

MiniMax M3.1 Flash

GPT-4o mini is comparatively weak here — only 128K context with an October 2023 knowledge cutoff

DeepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported)

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it carries the larger 1M tokens context.

Largest single-prompt input

MiniMax M3.1 Flash

Its 1M tokens window is about 7.8× larger than GPT-4o mini's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

→ MiniMax M3.1 Flash

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ MiniMax M3.1 Flash

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 $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

→ MiniMax M3.1 Flash

That is its strongest area.

An enterprise with regional data-residency rules

→ GPT-4o mini or MiniMax M3.1 Flash

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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.

MiniMax M3.1 Flash: where it fits

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. Released October 19, 2026 by MiniMax, it is built for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, 428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026, deepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported), and artificial Analysis Intelligence Index of 42, 13 points above MiniMax M3.

Its trade-offs: thinking is always on and default effort is max, which inflates output-token cost, behind DeepSeek V4.1 Flash 1017 on Artificial Analysis's independent index, custom MiniMax Community license: attribution required, and products above $20M annual revenue need written authorization, and benchmarks are mostly self-reported; no SWE-bench Verified figure published. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. MiniMax M3.1 Flash 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 MiniMax M3.1 Flash 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 pricing

Frequently asked questions

Is GPT-4o mini or MiniMax M3.1 Flash better for coding?

Public SWE-Bench figures are not available for either model, 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 MiniMax M3.1 Flash leans toward $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-4o mini or MiniMax M3.1 Flash?

MiniMax M3.1 Flash 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?

MiniMax M3.1 Flash — 1M tokens vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GPT-4o mini and MiniMax M3.1 Flash together?

Yes — a multi-model platform like LumiChats gives you GPT-4o mini, MiniMax M3.1 Flash 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 MiniMax M3.1 Flash?

MiniMax M3.1 Flash — released October 19, 2026, about 27 months after GPT-4o mini.

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.