Pairwise benchmark snapshot · Aug 16, 2026

Llama 3.3 70B vs OpenAI: o1 benchmark

Name: Llama 3.3 70B vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.3 70B (Meta) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 227 points (1,512 vs 1,285; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, OpenAI: o1 resolves 48.9% versus 45.1% (seed-bootstrap (Aug 1, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). OpenAI: o1 has the larger context window (200k 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 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.

Llama 3.3 70B (4)vsOpenAI: o1 (6)

Llama 3.3 70B: 4W (40%)Overall: OpenAI: o1OpenAI: o1: 6W (60%)

← Llama 3.3 70BOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,512

Coding Elo

—vs1,485

SWE-bench

45.1%vs48.9%

LiveBench

49.8%vs65.4%

GPQA Diamond

65.7%vs78%

Llama 3.3 70B: 0WOpenAI: o1: 5W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs45 tok/s

Time to first token

160 msvs420 ms

Llama 3.3 70B: 2WOpenAI: o1: 0W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$60/1M

Input price

$0.1/1Mvs$15/1M

Context window

131kvs200k

Llama 3.3 70B: 2WOpenAI: o1: 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 AdvantageOpenAI: o1 4/6

Preference Elo

OpenAI: o1

227 pts advantage

Throughput Speed

Llama 3.3 70B

95 tok/s faster

Price Efficiency

Llama 3.3 70B

$59.68/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,285

EloHigh is better

1,512+18%

Code Elo

High is better

—

Code EloHigh is better

1,485

LiveBench

High is better

49.8%

LiveBenchHigh is better

65.4%+31%

SWE-bench

High is better

45.1%

SWE-benchHigh is better

48.9%+8%

GPQA

High is better

65.7%

GPQAHigh is better

78%+19%

TTFT

Low is better

+163%160 ms

TTFTLow is better

420 ms

Speed

High is better

+211%140 tok/s

SpeedHigh is better

45 tok/s

In $

Low is better

+14900%$0.1/1M

In $Low is better

$15/1M

Out $

Low is better

+18650%$0.32/1M

Out $Low is better

$60/1M

Context

High is better

131k

ContextHigh is better

200k+53%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 99% 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)

Llama 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

OpenAI: o1$1,425.00 / mo

In: $525.00Out: $900.00

Estimated Cost Delta

Llama 3.3 70B is estimated to save $1,416.70/month ($17,000/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: o1:Pick OpenAI: o1 when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B when you are optimizing output cost.
3
Llama 3.3 70B:Pick Llama 3.3 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

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 RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
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	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

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.

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LIVE

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

OpenAI

OpenAI: o1

Elo 1,512

LiveBench65.4%

SWE-bench48.9%

Speed45 tok/s

Output cost$60/1M

Frequently Asked Questions

Which is better overall, Llama 3.3 70B or OpenAI: o1?: OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Llama 3.3 70B or OpenAI: o1?: OpenAI: o1 leads SWE-bench at 48.9% vs 45.1%. 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 $60/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.3 70B or OpenAI: o1?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: OpenAI: o1 accepts 200k 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. Llama 3.3 70B and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.3 70B matchups →|All OpenAI: o1 matchups →

Llama 3.3 70B vs Llama 3.1 70B (previous llama)Llama 3.3 70B vs Llama 4 Maverick (next llama)OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)GPT-5 vs OpenAI: o1 Llama 4 Maverick vs OpenAI: o1

Llama 3.3 70B vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.3 70B (4)vsOpenAI: o1 (6)

Llama 3.3 70B: 4W (40%)Overall: OpenAI: o1OpenAI: o1: 6W (60%)

← Llama 3.3 70BOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,512

Coding Elo

—vs1,485

SWE-bench

45.1%vs48.9%

LiveBench

49.8%vs65.4%

GPQA Diamond

65.7%vs78%

Llama 3.3 70B: 0WOpenAI: o1: 5W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs45 tok/s

Time to first token

160 msvs420 ms

Llama 3.3 70B: 2WOpenAI: o1: 0W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$60/1M

Input price

$0.1/1Mvs$15/1M

Context window

131kvs200k

Llama 3.3 70B: 2WOpenAI: o1: 1W

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

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 RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
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	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.3 70B or OpenAI: o1?

OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Llama 3.3 70B or OpenAI: o1?

OpenAI: o1 leads SWE-bench at 48.9% vs 45.1%. 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 $60/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.3 70B or OpenAI: o1?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

OpenAI: o1 accepts 200k 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. Llama 3.3 70B and OpenAI: o1 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

OpenAI: o1

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

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

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Llama 3.3 70B

OpenAI: o1

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

OpenAI: o1

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

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Llama 3.3 70B

OpenAI: o1

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons