Pairwise benchmark snapshot · Aug 17, 2026

Command A vs Llama 3.3 70B benchmark

Name: Command A vs Llama 3.3 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Command A (Cohere) and Llama 3.3 70B (Meta) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Command A is ahead by 59 points (1,344 vs 1,285; lmarena (Aug 17, 2026)). On SWE-bench coding, Llama 3.3 70B resolves 45.1% versus 42.6% (swebench (Aug 17, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). Command A has the larger context window (256k 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.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

Cohere

Command A

Cohere enterprise RAG and tool-use model.

Elo 1,344$10/1M out

Swap or Add Any Model to this Matchup

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.

Command A (4)vsLlama 3.3 70B (5)

Command A: 4W (44%)Overall: Llama 3.3 70BLlama 3.3 70B: 5W (56%)

← Command ALlama 3.3 70B →

Command A (3/5)

Intelligence & Reasoning

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

Preference Elo

1,344vs1,285

Coding Elo

—vs—

SWE-bench

42.6%vs45.1%

LiveBench

56.2%vs49.8%

GPQA Diamond

66%vs65.7%

Command A: 3WLlama 3.3 70B: 1W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

118 tok/svs140 tok/s

Time to first token

175 msvs160 ms

Command A: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$0.32/1M

Input price

$2.5/1Mvs$0.1/1M

Context window

256kvs131k

Command A: 1WLlama 3.3 70B: 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 AdvantageEven 3–3

Preference Elo

Command A

59 pts advantage

Throughput Speed

Llama 3.3 70B

22 tok/s faster

Price Efficiency

Llama 3.3 70B

$9.68/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+5%1,344

EloHigh is better

1,285

LiveBench

High is better

+13%56.2%

LiveBenchHigh is better

49.8%

SWE-bench

High is better

42.6%

SWE-benchHigh is better

45.1%+6%

GPQA

High is better

66%

GPQAHigh is better

65.7%

TTFT

Low is better

175 ms

TTFTLow is better

160 ms+9%

Speed

High is better

118 tok/s

SpeedHigh is better

140 tok/s+19%

In $

Low is better

$2.5/1M

In $Low is better

$0.1/1M+2400%

Out $

Low is better

$10/1M

Out $Low is better

$0.32/1M+3025%

Context

High is better

+95%256k

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 97% with Llama 3.3 70B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Command A$237.50 / mo

In: $87.50Out: $150.00

Llama 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

Estimated Cost Delta

Llama 3.3 70B is estimated to save $229.20/month ($2,750/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.3 70B:Pick Llama 3.3 70B when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B when time-to-first-token matters more than peak Elo.
3
Command A:Pick Command A as the default general assistant.

Recommended Workload Routing

Repo / coding agentsLlama 3.3 70B

Higher SWE-bench (45.1%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGCommand A

Larger window (256k).

Screenshots / visionCommand A

Command A is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Llama 3.3 70B	Higher SWE-bench (45.1%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Command A	Larger window (256k).
Screenshots / vision	Command A	Command A is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchMar 13, 2025

Command A: Cohere’s enterprise RAG and tool-use row

Mar 2025. RAG and tools, not a frontier Elo play. Listed so “command a benchmark” has a sourced page.

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/command-a-vs-llama-3-3-70b/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

Cohere

Command A

Elo 1,344

LiveBench56.2%

SWE-bench42.6%

Speed118 tok/s

Output cost$10/1M

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

Frequently Asked Questions

Which is better overall, Command A or Llama 3.3 70B?: Command A has the higher preference Elo in our latest snapshot (1,344). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Command A or Llama 3.3 70B?: Llama 3.3 70B leads SWE-bench at 45.1% vs 42.6%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.3 70B output tokens are $0.32/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, Command A or Llama 3.3 70B?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 175 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Command A accepts 256k tokens versus 131k.
Can I self-host either model?: Llama 3.3 70B 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. Command A and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Command A matchups →|All Llama 3.3 70B matchups →

Llama 3.3 70B vs Llama 3.1 70B (previous llama)Llama 3.3 70B vs Llama 4 Maverick (next llama)Llama 4 Maverick vs Command A Llama 3.1 405B vs Command A Llama 4 Scout vs Command A Mistral Large 3 vs Command A

Command A vs Llama 3.3 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Command A (4)vsLlama 3.3 70B (5)

Command A: 4W (44%)Overall: Llama 3.3 70BLlama 3.3 70B: 5W (56%)

← Command ALlama 3.3 70B →

Command A (3/5)

Intelligence & Reasoning

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

Preference Elo

1,344vs1,285

Coding Elo

—vs—

SWE-bench

42.6%vs45.1%

LiveBench

56.2%vs49.8%

GPQA Diamond

66%vs65.7%

Command A: 3WLlama 3.3 70B: 1W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

118 tok/svs140 tok/s

Time to first token

175 msvs160 ms

Command A: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$0.32/1M

Input price

$2.5/1Mvs$0.1/1M

Context window

256kvs131k

Command A: 1WLlama 3.3 70B: 2W

Recommended Workload Routing

Repo / coding agentsLlama 3.3 70B

Higher SWE-bench (45.1%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGCommand A

Larger window (256k).

Screenshots / visionCommand A

Command A is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Llama 3.3 70B	Higher SWE-bench (45.1%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Command A	Larger window (256k).
Screenshots / vision	Command A	Command A is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Command A or Llama 3.3 70B?

Command A has the higher preference Elo in our latest snapshot (1,344). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Command A or Llama 3.3 70B?

Llama 3.3 70B leads SWE-bench at 45.1% vs 42.6%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.32/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, Command A or Llama 3.3 70B?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 175 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Command A accepts 256k tokens versus 131k.

Can I self-host either model?

Llama 3.3 70B 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. Command A and Llama 3.3 70B 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

Command A

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Command A: Cohere’s enterprise RAG and tool-use row

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Command A

Llama 3.3 70B

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

Command A

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Command A: Cohere’s enterprise RAG and tool-use row

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Command A

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons