MiniMax vs GPT-4o: API Cost Compared (2026)

MiniMax-M2 runs roughly 10x cheaper than GPT-4o on both sides of the bill: about $0.255 input and $1.00 output per million tokens (measured via OpenRouter) against GPT-4o's $2.50 and $10.00, so the cost question turns on whether you need GPT-4o's ecosystem. That single ratio decides most of the budgeting, and the rest of this piece walks through where the gap is real and where GPT-4o still earns its premium.

The headline is simple. The trade-off underneath it is not, because price per token is only half of what you pay for. Let us put the numbers side by side first, then talk about what the spread actually buys you.

MiniMax-M2 vs GPT-4o API cost (verified 2026-06)

MiniMax-M2 is a Chinese frontier model that competes on token price rather than brand recognition, and the gap against GPT-4o is wide enough to change how you architect a high-volume app. According to MiniMax, MiniMax-M2 lists at $0.30 input and $1.20 output per million tokens on its international platform; the OpenRouter-routed rate we measured comes in slightly under that. Either way the comparison against GPT-4o holds.

Model	Input ($/1M)	Output ($/1M)	vs GPT-4o input	vs GPT-4o output
MiniMax-M2 (via OpenRouter)	$0.255	$1.00	~10x cheaper	10x cheaper
MiniMax-M2 (official MiniMax)	$0.30	$1.20	~8x cheaper	~8x cheaper
GPT-4o	$2.50	$10.00	baseline	baseline

The official MiniMax figure carries a needs_native_reverify flag because we sourced it through research rather than a native console login, so treat $0.30/$1.20 as the conservative number and the OpenRouter $0.255/$1.00 as what we actually billed against. Both sit roughly an order of magnitude below GPT-4o. The practical upshot is that on a workload dominated by output tokens, which is where GPT-4o's $10 rate bites hardest, switching to MiniMax-M2 cuts the dominant line of your bill by about 90%.

We called MiniMax-M2 ourselves (first-hand evidence)

A rate card tells you the price; it does not tell you what a real call costs once you account for token accounting and overhead. So we sent a request to MiniMax-M2 and watched the meter.

According to our evidence pack, a single general-purpose call to MiniMax-M2, routed through OpenRouter, used 39 input tokens and 200 output tokens and billed $0.000209945, returning in 5.08 seconds. A second coding-style call used 38 input and 200 output tokens at $0.0002514 in 2.95 seconds. Both general calls hit the 200-token output cap we set, so treat completion_tokens=200 as capped rather than the model's natural stopping point.

MiniMax-M2 call (via OpenRouter)	Input tokens	Output tokens	Billed cost	Latency
General	39	200	$0.000209945	5.08s
Coding	38	200	$0.0002514	2.95s

What stands out is the absolute scale: a complete 239-token exchange cost roughly two hundredths of a cent. The same exchange on GPT-4o, at $2.50 input and $10.00 output, would run about $0.0021, an order of magnitude more for the identical token count. We disclose the routing plainly: these are OpenRouter-billed numbers, not native MiniMax console charges, because MiniMax is reached here through OpenRouter rather than a direct key. The ratio against GPT-4o is what travels; the exact cents shift slightly between routing paths.

What the 10x gap actually buys, and what it costs you

A 10x price gap is not free money. The question every buyer should ask is what GPT-4o's premium is paying for, and whether your workload needs it.

GPT-4o's premium buys ecosystem: a mature SDK, deep tooling integration, function-calling reliability that thousands of production apps have stress-tested, and a support and compliance posture that enterprise procurement already trusts. According to OpenAI, GPT-4o is priced at $2.50 input and $10.00 output per million tokens, and that figure reflects a model positioned as a general-purpose default rather than a cost-optimized one. For a team shipping a customer-facing product where a single bad tool call is expensive, that maturity can be worth the 10x.

MiniMax-M2's value is the inverse. You trade some ecosystem familiarity for a bill that is roughly a tenth the size, which is the right trade when volume is high, the task is well-scoped, and you can validate output quality yourself. High-throughput summarization, classification, internal drafting, and batch processing are the workloads where the savings compound fastest and the ecosystem gap matters least.

If you want a third reference point on the cheap end, DeepSeek sits even lower than MiniMax. According to DeepSeek, its V4-Flash tier is $0.14 input and $0.28 output per million tokens, undercutting both MiniMax-M2 and GPT-4o on output by a wide margin. Our own anchor call to DeepSeek V4-Flash through its official endpoint used 27 input and 86 output tokens and returned in 1.96 seconds, so the low price does not come at the cost of responsiveness. The fuller DeepSeek picture lives in the DeepSeek API pricing hub; we mention it here only to place MiniMax on the spectrum.

The decision, stated plainly

Choose MiniMax-M2 when your bill is dominated by token volume, your task is well-defined, and you can verify quality without leaning on a battle-tested SDK. Choose GPT-4o when ecosystem maturity, tooling depth, or procurement comfort outweigh a 10x token premium, which is a real consideration for customer-facing or compliance-bound products. The number that should anchor the call is the output rate, because on most chat and generation workloads output dominates spend: $1.00 versus $10.00 per million is the gap you will feel every month.

One caution on context windows. Sources conflict on MiniMax's exact context limits across tiers, so we do not quote a specific number here; verify the current window in the MiniMax console before you architect around long-context behavior.

FAQ

How much cheaper is MiniMax-M2 than GPT-4o? About 10x on both input and output. MiniMax-M2 is roughly $0.255 input and $1.00 output per million tokens (measured via OpenRouter) versus GPT-4o's $2.50 and $10.00. On official MiniMax pricing the gap is about 8x, since MiniMax lists $0.30/$1.20.

What did a real MiniMax-M2 call cost in your test? A general call used 39 input and 200 output tokens and billed $0.000209945 in 5.08 seconds, measured via OpenRouter. The identical token count on GPT-4o would cost roughly $0.0021, about an order of magnitude more.

Is MiniMax available to US and international users? Yes. According to MiniMax pricing, the international platform at platform.minimax.io serves US and international users. Pricing we cite is OpenRouter-routed and the official figure needs native re-verification in the console.

When would GPT-4o still be the better cost choice? When the value of its mature ecosystem, function-calling reliability, and enterprise support outweighs the 10x token premium. For a low-volume, high-stakes, customer-facing app, that premium can be cheaper than the engineering risk of a less-proven stack.

Is DeepSeek cheaper than MiniMax-M2? On output, yes. DeepSeek V4-Flash lists at $0.14 input and $0.28 output per million tokens, below MiniMax-M2's $1.00 output. The two compete on different axes; see the DeepSeek API pricing hub for the full breakdown.

This is part of the MiniMax API pricing hub, where the full per-model rate card and methodology live.

Author: Kevin Fan, Customer Success Manager at China LLM Directory, specializing in Chinese LLM ecosystem pricing. Last verified: 2026-06-26.

MiniMax vs GPT-4o: API Cost Compared (2026)

MiniMax-M2 runs roughly 10x cheaper than GPT-4o on both sides of the bill: about $0.255 input and $1.00 output per million tokens (measured via…

Written by Fan Chuanyu

MiniMax-M2 vs GPT-4o API cost (verified 2026-06)

We called MiniMax-M2 ourselves (first-hand evidence)

What the 10x gap actually buys, and what it costs you

The decision, stated plainly

FAQ