Pairwise benchmark snapshot · Aug 17, 2026

Llama 3.1 70B vs MiniMax M2.5 benchmark

Name: Llama 3.1 70B vs MiniMax M2.5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 70B (Meta) and MiniMax M2.5 (MiniMax) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, MiniMax M2.5 is ahead by 238 points (1,478 vs 1,240; lmarena (Aug 17, 2026)). On SWE-bench coding, MiniMax M2.5 resolves 75.8% versus 40.2% (swebench (Aug 17, 2026)). Llama 3.1 70B streams faster (125 tok/s) while Llama 3.1 70B is the cheaper output token ($0.4/1M). 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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Model B

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (3)vsMiniMax M2.5 (7)

Llama 3.1 70B: 3W (30%)Overall: MiniMax M2.5MiniMax M2.5: 7W (70%)

← Llama 3.1 70BMiniMax M2.5 →

MiniMax M2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs1,478

Coding Elo

—vs1,536

SWE-bench

40.2%vs75.8%

LiveBench

46.8%vs64.4%

GPQA Diamond

48%vs76.2%

Llama 3.1 70B: 0WMiniMax M2.5: 5W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs90 tok/s

Time to first token

170 msvs250 ms

Llama 3.1 70B: 2WMiniMax M2.5: 0W

MiniMax M2.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.9/1M

Input price

$0.4/1Mvs$0.22/1M

Context window

131kvs205k

Llama 3.1 70B: 1WMiniMax M2.5: 2W

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 4/6

Preference Elo

MiniMax M2.5

238 pts advantage

Throughput Speed

Llama 3.1 70B

35 tok/s faster

Token Price Efficiency

Llama 3.1 70B

$0.50/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,240

EloHigh is better

1,478+19%

Code Elo

High is better

—

Code EloHigh is better

1,536

LiveBench

High is better

46.8%

LiveBenchHigh is better

64.4%+38%

SWE-bench

High is better

40.2%

SWE-benchHigh is better

75.8%+89%

GPQA

High is better

48%

GPQAHigh is better

76.2%+59%

TTFT

Low is better

+47%170 ms

TTFTLow is better

250 ms

Speed

High is better

+39%125 tok/s

SpeedHigh is better

90 tok/s

In $

Low is better

$0.40 / 1M

In $Low is better

$0.22 / 1M+82%

Out $

Low is better

+125%$0.40 / 1M

Out $Low is better

$0.90 / 1M

Context

High is better

131k

ContextHigh is better

205k+56%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 6% with Llama 3.1 70B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Llama 3.1 70B$20.00 / mo

In: $14Out: $6

MiniMax M2.5$21.20 / mo

In: $7.7Out: $13.5

Estimated Cost Delta

Llama 3.1 70B is estimated to save $1.20/month ($14/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
MiniMax M2.5:Pick MiniMax M2.5 when repo-level coding accuracy is the constraint.
2
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.
3
Llama 3.1 70B:Pick Llama 3.1 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsMiniMax M2.5

Higher SWE-bench (75.8%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

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	MiniMax M2.5	Higher SWE-bench (75.8%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
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.

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LIVE

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

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, Llama 3.1 70B or MiniMax M2.5?: MiniMax M2.5 has the higher preference Elo in our latest snapshot (1,478). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Llama 3.1 70B or MiniMax M2.5?: MiniMax M2.5 leads SWE-bench at 75.8% vs 40.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.1 70B output tokens are $0.4/1M versus $0.9/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 70B or MiniMax M2.5?: Llama 3.1 70B has the lower time-to-first-token (170 ms vs 250 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: MiniMax M2.5 accepts 205k tokens versus 131k.
Can I self-host either model?: Both Llama 3.1 70B and MiniMax M2.5 are marked open-weights. Hosting cost is not in this table.
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. Llama 3.1 70B 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 Llama 3.1 70B matchups →|All MiniMax M2.5 matchups →

Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Llama 4 Maverick vs MiniMax M2.5 Llama 3.1 405B vs MiniMax M2.5 Llama 4 Scout vs MiniMax M2.5 DeepSeek V4 Pro vs MiniMax M2.5

Llama 3.1 70B vs MiniMax M2.5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (3)vsMiniMax M2.5 (7)

Llama 3.1 70B: 3W (30%)Overall: MiniMax M2.5MiniMax M2.5: 7W (70%)

← Llama 3.1 70BMiniMax M2.5 →

MiniMax M2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs1,478

Coding Elo

—vs1,536

SWE-bench

40.2%vs75.8%

LiveBench

46.8%vs64.4%

GPQA Diamond

48%vs76.2%

Llama 3.1 70B: 0WMiniMax M2.5: 5W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs90 tok/s

Time to first token

170 msvs250 ms

Llama 3.1 70B: 2WMiniMax M2.5: 0W

MiniMax M2.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.9/1M

Input price

$0.4/1Mvs$0.22/1M

Context window

131kvs205k

Llama 3.1 70B: 1WMiniMax M2.5: 2W

Recommended Workload Routing

Repo / coding agentsMiniMax M2.5

Higher SWE-bench (75.8%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

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	MiniMax M2.5	Higher SWE-bench (75.8%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
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, Llama 3.1 70B or MiniMax M2.5?

MiniMax M2.5 has the higher preference Elo in our latest snapshot (1,478). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Llama 3.1 70B or MiniMax M2.5?

MiniMax M2.5 leads SWE-bench at 75.8% vs 40.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.1 70B output tokens are $0.4/1M versus $0.9/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 70B or MiniMax M2.5?

Llama 3.1 70B has the lower time-to-first-token (170 ms vs 250 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

MiniMax M2.5 accepts 205k tokens versus 131k.

Can I self-host either model?

Both Llama 3.1 70B and MiniMax M2.5 are marked open-weights. Hosting cost is not in this table.

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. Llama 3.1 70B and MiniMax M2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

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Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

MiniMax M2.5

Llama 3.1 70B

Llama 3.1 70B

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

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Llama 3.1 70B

MiniMax M2.5

Frequently Asked Questions

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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

MiniMax M2.5

Llama 3.1 70B

Llama 3.1 70B

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

Llama 3.1 70B

MiniMax M2.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons