Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Cohere: Command R7B (12-2024) vs DeepSeek V3 benchmark

Name: Cohere: Command R7B (12-2024) vs DeepSeek V3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Cohere: Command R7B (12-2024) (Cohere) and DeepSeek V3 (DeepSeek) using the latest snapshots we have as of Aug 16, 2026. Cohere: Command R7B (12-2024) wins on output price at $0.15/1M. DeepSeek V3 has the larger context window (164k 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 V3

Prior DeepSeek flagship. Baseline for v3 vs v4.

Elo 1,310$1.0287/1M 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 V3 (7)

Cohere: Command R7B (12-2024): 2W (22%)Overall: DeepSeek V3DeepSeek V3: 7W (78%)

← Cohere: Command R7B (12-2024)DeepSeek V3 →

DeepSeek V3 (4/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,310

Coding Elo

—vs—

SWE-bench

—vs48.6%

LiveBench

—vs55.4%

GPQA Diamond

—vs59.1%

Cohere: Command R7B (12-2024): 0WDeepSeek V3: 4W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs66 tok/s

Time to first token

—vs380 ms

Cohere: Command R7B (12-2024): 0WDeepSeek V3: 2W

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

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.15/1Mvs$1.03/1M

Input price

$0.04/1Mvs$0.26/1M

Context window

128kvs164k

Cohere: Command R7B (12-2024): 2WDeepSeek V3: 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 AdvantageDeepSeek V3 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Cohere: Command R7B (12-2024)

$0.88/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,310

LiveBench

High is better

—

LiveBenchHigh is better

55.4%

SWE-bench

High is better

—

SWE-benchHigh is better

48.6%

GPQA

High is better

—

GPQAHigh is better

59.1%

TTFT

Low is better

—

TTFTLow is better

380 ms

Speed

High is better

—

SpeedHigh is better

66 tok/s

In $

Low is better

+586%$0.04/1M

In $Low is better

$0.26/1M

Out $

Low is better

+586%$0.15/1M

Out $Low is better

$1.03/1M

Context

High is better

128k

ContextHigh is better

164k+28%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 85% 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 V3$24.44 / mo

In: $9.01Out: $15.43

Estimated Cost Delta

Cohere: Command R7B (12-2024) is estimated to save $20.88/month ($251/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 RAGDeepSeek V3

Larger window (164k).

Screenshots / visionDeepSeek V3

DeepSeek V3 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	DeepSeek V3	Larger window (164k).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

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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 V3

Elo 1,310

LiveBench55.4%

SWE-bench48.6%

Speed66 tok/s

Output cost$1.03/1M

Frequently Asked Questions

Which is better overall, Cohere: Command R7B (12-2024) or DeepSeek V3?: 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 V3?: 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 $1.03/1M. Input prices and retry rates still move the real bill.
Which is faster, Cohere: Command R7B (12-2024) or DeepSeek V3?: We do not have TTFT for both models.
Which has the larger context window?: DeepSeek V3 accepts 164k tokens versus 128k.
Can I self-host either model?: DeepSeek V3 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 V3 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 V3 matchups →

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

Cohere: Command R7B (12-2024) vs DeepSeek V3 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 V3 (7)

Cohere: Command R7B (12-2024): 2W (22%)Overall: DeepSeek V3DeepSeek V3: 7W (78%)

← Cohere: Command R7B (12-2024)DeepSeek V3 →

DeepSeek V3 (4/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,310

Coding Elo

—vs—

SWE-bench

—vs48.6%

LiveBench

—vs55.4%

GPQA Diamond

—vs59.1%

Cohere: Command R7B (12-2024): 0WDeepSeek V3: 4W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs66 tok/s

Time to first token

—vs380 ms

Cohere: Command R7B (12-2024): 0WDeepSeek V3: 2W

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

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.15/1Mvs$1.03/1M

Input price

$0.04/1Mvs$0.26/1M

Context window

128kvs164k

Cohere: Command R7B (12-2024): 2WDeepSeek V3: 1W

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 RAGDeepSeek V3

Larger window (164k).

Screenshots / visionDeepSeek V3

DeepSeek V3 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	DeepSeek V3	Larger window (164k).
Screenshots / vision	DeepSeek V3	DeepSeek V3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Cohere: Command R7B (12-2024) or DeepSeek V3?

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 V3?

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 $1.03/1M. Input prices and retry rates still move the real bill.

Which is faster, Cohere: Command R7B (12-2024) or DeepSeek V3?

We do not have TTFT for both models.

Which has the larger context window?

DeepSeek V3 accepts 164k tokens versus 128k.

Can I self-host either model?

DeepSeek V3 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 V3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.