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

OpenAI: GPT-4o-mini (batch) vs MiniMax M2.5 benchmark

Name: OpenAI: GPT-4o-mini (batch) vs MiniMax M2.5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: GPT-4o-mini (batch) (OpenAI) and MiniMax M2.5 (MiniMax) using the latest snapshots we have as of Aug 17, 2026. OpenAI: GPT-4o-mini (batch) wins on output price at $0.3/1M. MiniMax M2.5 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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OpenAI

OpenAI: GPT-4o-mini (batch)

Auto-discovered from OpenRouter (openai/gpt-4o-mini:batch). Preview until a second source matches.

$0.3/1M out

MiniMax

MiniMax M2.5

MiniMax coding model. Tied near the top of official SWE-bench bash-only in Feb 2026.

Elo 1,478$0.8999999999999999/1M out

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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.

OpenAI: GPT-4o-mini (batch) (2)vsMiniMax M2.5 (8)

OpenAI: GPT-4o-mini (batch): 2W (20%)Overall: MiniMax M2.5MiniMax M2.5: 8W (80%)

← OpenAI: GPT-4o-mini (batch)MiniMax M2.5 →

MiniMax M2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,478

Coding Elo

—vs1,536

SWE-bench

—vs75.8%

LiveBench

—vs64.4%

GPQA Diamond

—vs76.2%

OpenAI: GPT-4o-mini (batch): 0WMiniMax M2.5: 5W

MiniMax M2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs90 tok/s

Time to first token

—vs250 ms

OpenAI: GPT-4o-mini (batch): 0WMiniMax M2.5: 2W

OpenAI: GPT-4o-mini (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.3/1Mvs$0.9/1M

Input price

$0.075/1Mvs$0.22/1M

Context window

128kvs205k

OpenAI: GPT-4o-mini (batch): 2WMiniMax M2.5: 1W

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 AdvantageMiniMax M2.5 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

OpenAI: GPT-4o-mini (batch)

$0.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,478

Code Elo

High is better

—

Code EloHigh is better

1,536

LiveBench

High is better

—

LiveBenchHigh is better

64.4%

SWE-bench

High is better

—

SWE-benchHigh is better

75.8%

GPQA

High is better

—

GPQAHigh is better

76.2%

TTFT

Low is better

—

TTFTLow is better

250 ms

Speed

High is better

—

SpeedHigh is better

90 tok/s

In $

Low is better

+193%$0.08 / 1M

In $Low is better

$0.22 / 1M

Out $

Low is better

+200%$0.30 / 1M

Out $Low is better

$0.90 / 1M

Context

High is better

128k

ContextHigh is better

205k+60%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 66% with OpenAI: GPT-4o-mini (batch)

Monthly Volume50M tokens/mo

Quick Presets:

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

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

OpenAI: GPT-4o-mini (batch)$7.13 / mo

In: $2.63Out: $4.5

MiniMax M2.5$21.20 / mo

In: $7.7Out: $13.5

Estimated Cost Delta

OpenAI: GPT-4o-mini (batch) is estimated to save $14.08/month ($169/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: GPT-4o-mini (batch):Pick OpenAI: GPT-4o-mini (batch) when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-4o-mini (batch)

Lower output list price ($0.3/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMiniMax M2.5

Larger window (205k).

Screenshots / visionMiniMax M2.5

MiniMax M2.5 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	OpenAI: GPT-4o-mini (batch)	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MiniMax M2.5	Larger window (205k).
Screenshots / vision	MiniMax M2.5	MiniMax M2.5 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchFeb 12, 2026

MiniMax M2.5: the Feb 2026 SWE-bench specialist still on the board

Tied near the top of official SWE-bench bash-only in Feb 2026. Coding lists should still see it.

Community Sentiment

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LIVE

OpenAI

OpenAI: GPT-4o-mini (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.3/1M

MiniMax

MiniMax M2.5

Elo 1,478

LiveBench64.4%

SWE-bench75.8%

Speed90 tok/s

Output cost$0.9/1M

Frequently Asked Questions

Which is better overall, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?: 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, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?: 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?: OpenAI: GPT-4o-mini (batch) output tokens are $0.3/1M versus $0.9/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?: We do not have TTFT for both models.
Which has the larger context window?: MiniMax M2.5 accepts 205k tokens versus 128k.
Can I self-host either model?: MiniMax M2.5 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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. OpenAI: GPT-4o-mini (batch) and MiniMax M2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: GPT-4o-mini (batch) matchups →|All MiniMax M2.5 matchups →

GPT-5 vs MiniMax M2.5 GPT-4.5 Orion vs MiniMax M2.5 GPT-5.6 Sol vs MiniMax M2.5 GPT-5.6 Terra vs MiniMax M2.5 GPT-5 mini vs MiniMax M2.5 GPT-4o vs MiniMax M2.5

OpenAI: GPT-4o-mini (batch) vs MiniMax M2.5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-4o-mini (batch) (2)vsMiniMax M2.5 (8)

OpenAI: GPT-4o-mini (batch): 2W (20%)Overall: MiniMax M2.5MiniMax M2.5: 8W (80%)

← OpenAI: GPT-4o-mini (batch)MiniMax M2.5 →

MiniMax M2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,478

Coding Elo

—vs1,536

SWE-bench

—vs75.8%

LiveBench

—vs64.4%

GPQA Diamond

—vs76.2%

OpenAI: GPT-4o-mini (batch): 0WMiniMax M2.5: 5W

MiniMax M2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs90 tok/s

Time to first token

—vs250 ms

OpenAI: GPT-4o-mini (batch): 0WMiniMax M2.5: 2W

OpenAI: GPT-4o-mini (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.3/1Mvs$0.9/1M

Input price

$0.075/1Mvs$0.22/1M

Context window

128kvs205k

OpenAI: GPT-4o-mini (batch): 2WMiniMax M2.5: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-4o-mini (batch)

Lower output list price ($0.3/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMiniMax M2.5

Larger window (205k).

Screenshots / visionMiniMax M2.5

MiniMax M2.5 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	OpenAI: GPT-4o-mini (batch)	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MiniMax M2.5	Larger window (205k).
Screenshots / vision	MiniMax M2.5	MiniMax M2.5 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?

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, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?

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?

OpenAI: GPT-4o-mini (batch) output tokens are $0.3/1M versus $0.9/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: GPT-4o-mini (batch) or MiniMax M2.5?

We do not have TTFT for both models.

Which has the larger context window?

MiniMax M2.5 accepts 205k tokens versus 128k.

Can I self-host either model?

MiniMax M2.5 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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. OpenAI: GPT-4o-mini (batch) and MiniMax M2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

OpenAI: GPT-4o-mini (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

MiniMax M2.5: the Feb 2026 SWE-bench specialist still on the board

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

OpenAI: GPT-4o-mini (batch)

MiniMax M2.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

OpenAI: GPT-4o-mini (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

MiniMax M2.5: the Feb 2026 SWE-bench specialist still on the board

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

OpenAI: GPT-4o-mini (batch)

MiniMax M2.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons