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

Cohere: Command R (08-2024) vs Gemini 2.5 Pro benchmark

Name: Cohere: Command R (08-2024) vs Gemini 2.5 Pro AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Cohere: Command R (08-2024) (Cohere) and Gemini 2.5 Pro (Google) using the latest snapshots we have as of Aug 17, 2026. Cohere: Command R (08-2024) wins on output price at $0.6/1M. Gemini 2.5 Pro has the larger context window (1M 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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Cohere

Cohere: Command R (08-2024)

Auto-discovered from OpenRouter (cohere/command-r-08-2024). Preview until a second source matches.

$0.6/1M out

Google

Gemini 2.5 Pro

Previous Google long-context flagship.

Elo 1,350$10/1M out

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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.

Cohere: Command R (08-2024) (2)vsGemini 2.5 Pro (7)

Cohere: Command R (08-2024): 2W (22%)Overall: Gemini 2.5 ProGemini 2.5 Pro: 7W (78%)

← Cohere: Command R (08-2024)Gemini 2.5 Pro →

Gemini 2.5 Pro (4/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,350

Coding Elo

—vs—

SWE-bench

—vs63.8%

LiveBench

—vs57.1%

GPQA Diamond

—vs76.4%

Cohere: Command R (08-2024): 0WGemini 2.5 Pro: 4W

Gemini 2.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs95 tok/s

Time to first token

—vs210 ms

Cohere: Command R (08-2024): 0WGemini 2.5 Pro: 2W

Cohere: Command R (08-2024) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.6/1Mvs$10/1M

Input price

$0.15/1Mvs$1.25/1M

Context window

128kvs1M

Cohere: Command R (08-2024): 2WGemini 2.5 Pro: 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 AdvantageGemini 2.5 Pro 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

Cohere: Command R (08-2024)

$9.40/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,350

LiveBench

High is better

—

LiveBenchHigh is better

57.1%

SWE-bench

High is better

—

SWE-benchHigh is better

63.8%

GPQA

High is better

—

GPQAHigh is better

76.4%

TTFT

Low is better

—

TTFTLow is better

210 ms

Speed

High is better

—

SpeedHigh is better

95 tok/s

In $

Low is better

+733%$0.15 / 1M

In $Low is better

$1.25 / 1M

Out $

Low is better

+1567%$0.60 / 1M

Out $Low is better

$10.00 / 1M

Context

High is better

128k

ContextHigh is better

1M+719%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 93% with Cohere: Command R (08-2024)

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Cohere: Command R (08-2024)$14.25 / mo

In: $5.25Out: $9

Gemini 2.5 Pro$193.75 / mo

In: $43.75Out: $150

Estimated Cost Delta

Cohere: Command R (08-2024) is estimated to save $179.50/month ($2,154/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Cohere: Command R (08-2024):Pick Cohere: Command R (08-2024) when you are optimizing output cost.
2
Gemini 2.5 Pro:Pick Gemini 2.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatCohere: Command R (08-2024)

Lower output list price ($0.6/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 2.5 Pro

Larger window (1M).

Screenshots / visionGemini 2.5 Pro

Gemini 2.5 Pro 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	Cohere: Command R (08-2024)	Lower output list price ($0.6/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 2.5 Pro	Larger window (1M).
Screenshots / vision	Gemini 2.5 Pro	Gemini 2.5 Pro is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchMar 25, 2025

Gemini 2.5 Pro remains the previous Google long-context flagship

2025-03 row. 3.x Pro replaced it. Kept for old “2.5 pro vs gpt-4o” inbound.

Community Sentiment

Cast Your Matchup Vote

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LIVE

Cohere

Cohere: Command R (08-2024)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.6/1M

Google

Gemini 2.5 Pro

Elo 1,350

LiveBench57.1%

SWE-bench63.8%

Speed95 tok/s

Output cost$10/1M

Frequently Asked Questions

Which is better overall, Cohere: Command R (08-2024) or Gemini 2.5 Pro?: 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, Cohere: Command R (08-2024) or Gemini 2.5 Pro?: 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?: Cohere: Command R (08-2024) output tokens are $0.6/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, Cohere: Command R (08-2024) or Gemini 2.5 Pro?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 2.5 Pro accepts 1M tokens versus 128k.
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. Cohere: Command R (08-2024) and Gemini 2.5 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Cohere: Command R (08-2024) matchups →|All Gemini 2.5 Pro matchups →

Gemini 2.5 Pro vs Gemini 1.5 Pro (previous gemini-pro)Gemini 2.5 Pro vs Gemini 3 Pro (next gemini-pro)Gemini 3 Flash vs Gemini 2.5 Pro Gemini 2.0 Flash Thinking vs Gemini 2.5 Pro Gemini 3.6 Pro vs Gemini 2.5 Pro Gemini 3.6 Flash vs Gemini 2.5 Pro

Cohere: Command R (08-2024) vs Gemini 2.5 Pro benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Cohere: Command R (08-2024) (2)vsGemini 2.5 Pro (7)

Cohere: Command R (08-2024): 2W (22%)Overall: Gemini 2.5 ProGemini 2.5 Pro: 7W (78%)

← Cohere: Command R (08-2024)Gemini 2.5 Pro →

Gemini 2.5 Pro (4/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,350

Coding Elo

—vs—

SWE-bench

—vs63.8%

LiveBench

—vs57.1%

GPQA Diamond

—vs76.4%

Cohere: Command R (08-2024): 0WGemini 2.5 Pro: 4W

Gemini 2.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs95 tok/s

Time to first token

—vs210 ms

Cohere: Command R (08-2024): 0WGemini 2.5 Pro: 2W

Cohere: Command R (08-2024) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.6/1Mvs$10/1M

Input price

$0.15/1Mvs$1.25/1M

Context window

128kvs1M

Cohere: Command R (08-2024): 2WGemini 2.5 Pro: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatCohere: Command R (08-2024)

Lower output list price ($0.6/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 2.5 Pro

Larger window (1M).

Screenshots / visionGemini 2.5 Pro

Gemini 2.5 Pro 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	Cohere: Command R (08-2024)	Lower output list price ($0.6/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 2.5 Pro	Larger window (1M).
Screenshots / vision	Gemini 2.5 Pro	Gemini 2.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Cohere: Command R (08-2024) or Gemini 2.5 Pro?

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, Cohere: Command R (08-2024) or Gemini 2.5 Pro?

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?

Cohere: Command R (08-2024) output tokens are $0.6/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, Cohere: Command R (08-2024) or Gemini 2.5 Pro?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 2.5 Pro accepts 1M tokens versus 128k.

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. Cohere: Command R (08-2024) and Gemini 2.5 Pro 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

Cohere: Command R (08-2024)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini 2.5 Pro remains the previous Google long-context flagship

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Cohere: Command R (08-2024)

Gemini 2.5 Pro

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

Cohere: Command R (08-2024)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini 2.5 Pro remains the previous Google long-context flagship

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Cohere: Command R (08-2024)

Gemini 2.5 Pro

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons