Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

MiniMax: MiniMax M2.1 vs OpenAI: o3 Mini benchmark

Name: MiniMax: MiniMax M2.1 vs OpenAI: o3 Mini AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares MiniMax: MiniMax M2.1 (MiniMax) and OpenAI: o3 Mini (OpenAI) using the latest snapshots we have as of Aug 17, 2026. MiniMax: MiniMax M2.1 wins on output price at $1.2/1M. MiniMax: MiniMax M2.1 has the larger context window (205k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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MiniMax

MiniMax: MiniMax M2.1

Auto-discovered from OpenRouter (minimax/minimax-m2.1). Preview until a second source matches.

$1.2/1M out

OpenAI

OpenAI: o3 Mini

Auto-discovered from OpenRouter (openai/o3-mini). Preview until a second source matches.

o3-miniElo 1,560$4.4/1M out

OpenAI: o3 Mini Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

OpenAI: o3 Mini Reasoning Scaler & Token Billing Simulator

Param: reasoning_effort

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

290 ms

1.0x (Standard eval)

CoT Thinking Tokens

~1,500

Billed as output tokens

Est. Cost / 1k Calls

$8.47

4.5x base invoice

STEM / SWE Boost

+4.5%

GPQA & SWE-bench scaling

Medium (Standard) Operational Profile

Default evaluation baseline balancing reasoning depth with stream rate.

Direct Provider APIOpenAI

"reasoning_effort": "medium"

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "medium",
  "max_tokens": 1500
}

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Instant pairwise benchmark recalculation across 400+ LLMs

Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

MiniMax: MiniMax M2.1 (3)vsOpenAI: o3 Mini (7)

MiniMax: MiniMax M2.1: 3W (30%)Overall: OpenAI: o3 MiniOpenAI: o3 Mini: 7W (70%)

← MiniMax: MiniMax M2.1OpenAI: o3 Mini →

OpenAI: o3 Mini (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,560

Coding Elo

—vs1,585

SWE-bench

—vs78.5%

LiveBench

—vs72.4%

GPQA Diamond

—vs87.2%

MiniMax: MiniMax M2.1: 0WOpenAI: o3 Mini: 5W

OpenAI: o3 Mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs92 tok/s

Time to first token

—vs290 ms

MiniMax: MiniMax M2.1: 0WOpenAI: o3 Mini: 2W

MiniMax: MiniMax M2.1 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.2/1Mvs$4.4/1M

Input price

$0.3/1Mvs$1.1/1M

Context window

205kvs200k

MiniMax: MiniMax M2.1: 3WOpenAI: o3 Mini: 0W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageOpenAI: o3 Mini 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

MiniMax: MiniMax M2.1

$3.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,560

Code Elo

High is better

—

Code EloHigh is better

1,585

LiveBench

High is better

—

LiveBenchHigh is better

72.4%

SWE-bench

High is better

—

SWE-benchHigh is better

78.5%

GPQA

High is better

—

GPQAHigh is better

87.2%

TTFT

Low is better

—

TTFTLow is better

290 ms

Speed

High is better

—

SpeedHigh is better

92 tok/s

In $

Low is better

+267%$0.30 / 1M

In $Low is better

$1.10 / 1M

Out $

Low is better

+267%$1.20 / 1M

Out $Low is better

$4.40 / 1M

Context

High is better

+2%205k

ContextHigh is better

200k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 73% with MiniMax: MiniMax M2.1

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

MiniMax: MiniMax M2.1$28.50 / mo

In: $10.5Out: $18

OpenAI: o3 Mini$104.50 / mo

In: $38.5Out: $66

Estimated Cost Delta

MiniMax: MiniMax M2.1 is estimated to save $76.00/month ($912/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
MiniMax: MiniMax M2.1:Pick MiniMax: MiniMax M2.1 when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatMiniMax: MiniMax M2.1

Lower output list price ($1.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMiniMax: MiniMax M2.1

Larger window (205k).

Screenshots / visionOpenAI: o3 Mini

OpenAI: o3 Mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	MiniMax: MiniMax M2.1	Lower output list price ($1.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MiniMax: MiniMax M2.1	Larger window (205k).
Screenshots / vision	OpenAI: o3 Mini	OpenAI: o3 Mini is the side marked multimodal in the catalog.

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CompareLLM Matrix

Verified head-to-head card

LIVE

MiniMax

MiniMax: MiniMax M2.1

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$1.2/1M

OpenAI

OpenAI: o3 Mini

Elo 1,560

LiveBench72.4%

SWE-bench78.5%

Speed92 tok/s

Output cost$4.4/1M

Frequently Asked Questions

Which is better overall, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: MiniMax: MiniMax M2.1 output tokens are $1.2/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?: We do not have TTFT for both models.
Which has the larger context window?: MiniMax: MiniMax M2.1 accepts 205k tokens versus 200k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. MiniMax: MiniMax M2.1 and OpenAI: o3 Mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All MiniMax: MiniMax M2.1 matchups →|All OpenAI: o3 Mini matchups →

OpenAI: o3 Mini vs OpenAI o1-mini (previous gpt-mini)OpenAI: o3 Mini vs GPT-5 mini (next gpt-mini)GPT-5 vs OpenAI: o3 Mini GPT-4.5 Orion vs OpenAI: o3 Mini GPT-5.6 Sol vs OpenAI: o3 Mini GPT-5.6 Terra vs OpenAI: o3 Mini

MiniMax: MiniMax M2.1 vs OpenAI: o3 Mini benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

MiniMax: MiniMax M2.1 (3)vsOpenAI: o3 Mini (7)

MiniMax: MiniMax M2.1: 3W (30%)Overall: OpenAI: o3 MiniOpenAI: o3 Mini: 7W (70%)

← MiniMax: MiniMax M2.1OpenAI: o3 Mini →

OpenAI: o3 Mini (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,560

Coding Elo

—vs1,585

SWE-bench

—vs78.5%

LiveBench

—vs72.4%

GPQA Diamond

—vs87.2%

MiniMax: MiniMax M2.1: 0WOpenAI: o3 Mini: 5W

OpenAI: o3 Mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs92 tok/s

Time to first token

—vs290 ms

MiniMax: MiniMax M2.1: 0WOpenAI: o3 Mini: 2W

MiniMax: MiniMax M2.1 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.2/1Mvs$4.4/1M

Input price

$0.3/1Mvs$1.1/1M

Context window

205kvs200k

MiniMax: MiniMax M2.1: 3WOpenAI: o3 Mini: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatMiniMax: MiniMax M2.1

Lower output list price ($1.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMiniMax: MiniMax M2.1

Larger window (205k).

Screenshots / visionOpenAI: o3 Mini

OpenAI: o3 Mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	MiniMax: MiniMax M2.1	Lower output list price ($1.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MiniMax: MiniMax M2.1	Larger window (205k).
Screenshots / vision	OpenAI: o3 Mini	OpenAI: o3 Mini is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

MiniMax: MiniMax M2.1 output tokens are $1.2/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, MiniMax: MiniMax M2.1 or OpenAI: o3 Mini?

We do not have TTFT for both models.

Which has the larger context window?

MiniMax: MiniMax M2.1 accepts 205k tokens versus 200k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. MiniMax: MiniMax M2.1 and OpenAI: o3 Mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.