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

Cohere: Command R7B (12-2024) vs DeepSeek Coder V2 benchmark

Name: Cohere: Command R7B (12-2024) vs DeepSeek Coder V2 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Cohere: Command R7B (12-2024) (Cohere) and DeepSeek Coder V2 (DeepSeek) using the latest snapshots we have as of Aug 17, 2026. Cohere: Command R7B (12-2024) wins on output price at $0.15/1M. Cohere: Command R7B (12-2024) has the larger context window (128k 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 R7B (12-2024)

Auto-discovered from OpenRouter (cohere/command-r7b-12-2024). Preview until a second source matches.

$0.15/1M out

DeepSeek

DeepSeek Coder V2

DeepSeek open-weight Mixture-of-Experts coding model supporting 338 programming languages and 128k context.

Elo 1,365$0.28/1M 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 R7B (12-2024) (2)vsDeepSeek Coder V2 (7)

Cohere: Command R7B (12-2024): 2W (22%)Overall: DeepSeek Coder V2DeepSeek Coder V2: 7W (78%)

← Cohere: Command R7B (12-2024)1 tiesDeepSeek Coder V2 →

DeepSeek Coder V2 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,365

Coding Elo

—vs1,442

SWE-bench

—vs60.5%

LiveBench

—vs58.6%

GPQA Diamond

—vs70.2%

Cohere: Command R7B (12-2024): 0WDeepSeek Coder V2: 5W

DeepSeek Coder V2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs75 tok/s

Time to first token

—vs290 ms

Cohere: Command R7B (12-2024): 0WDeepSeek Coder V2: 2W

Cohere: Command R7B (12-2024) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.15/1Mvs$0.28/1M

Input price

$0.04/1Mvs$0.14/1M

Context window

128kvs128k

Cohere: Command R7B (12-2024): 2WDeepSeek Coder V2: 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 AdvantageDeepSeek Coder V2 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Cohere: Command R7B (12-2024)

$0.13/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,365

Code Elo

High is better

—

Code EloHigh is better

1,442

LiveBench

High is better

—

LiveBenchHigh is better

58.6%

SWE-bench

High is better

—

SWE-benchHigh is better

60.5%

GPQA

High is better

—

GPQAHigh is better

70.2%

TTFT

Low is better

—

TTFTLow is better

290 ms

Speed

High is better

—

SpeedHigh is better

75 tok/s

In $

Low is better

+273%$0.04/1M

In $Low is better

$0.14/1M

Out $

Low is better

+87%$0.15/1M

Out $Low is better

$0.28/1M

Context

High is better

128k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 61% with Cohere: Command R7B (12-2024)

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 R7B (12-2024)$3.56 / mo

In: $1.31Out: $2.25

DeepSeek Coder V2$9.10 / mo

In: $4.90Out: $4.20

Estimated Cost Delta

Cohere: Command R7B (12-2024) is estimated to save $5.54/month ($66/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Cohere: Command R7B (12-2024):Pick Cohere: Command R7B (12-2024) when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatCohere: Command R7B (12-2024)

Lower output list price ($0.15/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGCohere: Command R7B (12-2024)

Larger window (128k).

Screenshots / visionDeepSeek Coder V2

DeepSeek Coder V2 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 R7B (12-2024)	Lower output list price ($0.15/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Cohere: Command R7B (12-2024)	Larger window (128k).
Screenshots / vision	DeepSeek Coder V2	DeepSeek Coder V2 is the side marked multimodal in the catalog.

Community Sentiment

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CompareLLM Matrix

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LIVE

Cohere

Cohere: Command R7B (12-2024)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.15/1M

DeepSeek

DeepSeek Coder V2

Elo 1,365

LiveBench58.6%

SWE-bench60.5%

Speed75 tok/s

Output cost$0.28/1M

Frequently Asked Questions

Which is better overall, Cohere: Command R7B (12-2024) or DeepSeek Coder V2?: 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 R7B (12-2024) or DeepSeek Coder V2?: 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 R7B (12-2024) output tokens are $0.15/1M versus $0.28/1M. Input prices and retry rates still move the real bill.
Which is faster, Cohere: Command R7B (12-2024) or DeepSeek Coder V2?: We do not have TTFT for both models.
Which has the larger context window?: Cohere: Command R7B (12-2024) accepts 128k tokens versus 128k.
Can I self-host either model?: DeepSeek Coder V2 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. Cohere: Command R7B (12-2024) and DeepSeek Coder V2 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Cohere: Command R7B (12-2024) matchups →|All DeepSeek Coder V2 matchups →

DeepSeek Coder V2 vs DeepSeek V3 (next deepseek)DeepSeek V4 Pro vs DeepSeek Coder V2 DeepSeek V4 Flash vs DeepSeek Coder V2 DeepSeek R1 vs DeepSeek Coder V2 Command A vs DeepSeek Coder V2 Claude Opus 4.5 vs DeepSeek Coder V2

Cohere: Command R7B (12-2024) vs DeepSeek Coder V2 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Cohere: Command R7B (12-2024) (2)vsDeepSeek Coder V2 (7)

Cohere: Command R7B (12-2024): 2W (22%)Overall: DeepSeek Coder V2DeepSeek Coder V2: 7W (78%)

← Cohere: Command R7B (12-2024)1 tiesDeepSeek Coder V2 →

DeepSeek Coder V2 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,365

Coding Elo

—vs1,442

SWE-bench

—vs60.5%

LiveBench

—vs58.6%

GPQA Diamond

—vs70.2%

Cohere: Command R7B (12-2024): 0WDeepSeek Coder V2: 5W

DeepSeek Coder V2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs75 tok/s

Time to first token

—vs290 ms

Cohere: Command R7B (12-2024): 0WDeepSeek Coder V2: 2W

Cohere: Command R7B (12-2024) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.15/1Mvs$0.28/1M

Input price

$0.04/1Mvs$0.14/1M

Context window

128kvs128k

Cohere: Command R7B (12-2024): 2WDeepSeek Coder V2: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatCohere: Command R7B (12-2024)

Lower output list price ($0.15/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGCohere: Command R7B (12-2024)

Larger window (128k).

Screenshots / visionDeepSeek Coder V2

DeepSeek Coder V2 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 R7B (12-2024)	Lower output list price ($0.15/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Cohere: Command R7B (12-2024)	Larger window (128k).
Screenshots / vision	DeepSeek Coder V2	DeepSeek Coder V2 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Cohere: Command R7B (12-2024) or DeepSeek Coder V2?

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 R7B (12-2024) or DeepSeek Coder V2?

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 R7B (12-2024) output tokens are $0.15/1M versus $0.28/1M. Input prices and retry rates still move the real bill.

Which is faster, Cohere: Command R7B (12-2024) or DeepSeek Coder V2?

We do not have TTFT for both models.

Which has the larger context window?

Cohere: Command R7B (12-2024) accepts 128k tokens versus 128k.

Can I self-host either model?

DeepSeek Coder V2 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. Cohere: Command R7B (12-2024) and DeepSeek Coder V2 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.