Pairwise benchmark snapshot · Aug 16, 2026

Grok 4.6 vs Llama 3.3 70B benchmark

Name: Grok 4.6 vs Llama 3.3 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Grok 4.6 (xAI) and Llama 3.3 70B (Meta) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Grok 4.6 is ahead by 307 points (1,592 vs 1,285; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, Grok 4.6 resolves 69.1% versus 45.1% (seed-bootstrap (Aug 16, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). Grok 4.6 has the larger context window (500k 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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xAI

Grok 4.6

xAI Aug 12 2026 post-training refresh of Grok 4.5. Same $2/$6 API price, 500k context, stronger agentic traces.

Elo 1,592$6/1M 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.

Grok 4.6 (6)vsLlama 3.3 70B (4)

Grok 4.6: 6W (60%)Overall: Grok 4.6Llama 3.3 70B: 4W (40%)

← Grok 4.6Llama 3.3 70B →

Grok 4.6 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,592vs1,285

Coding Elo

1,568vs—

SWE-bench

69.1%vs45.1%

LiveBench

71.4%vs49.8%

GPQA Diamond

84.6%vs65.7%

Grok 4.6: 5WLlama 3.3 70B: 0W

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

195 msvs160 ms

Grok 4.6: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$6/1Mvs$0.32/1M

Input price

$2/1Mvs$0.1/1M

Context window

500kvs131k

Grok 4.6: 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 AdvantageGrok 4.6 4/6

Preference Elo

Grok 4.6

307 pts advantage

Throughput Speed

Llama 3.3 70B

22 tok/s faster

Price Efficiency

Llama 3.3 70B

$5.68/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+24%1,592

EloHigh is better

1,285

Code Elo

High is better

1,568

Code EloHigh is better

—

LiveBench

High is better

+43%71.4%

LiveBenchHigh is better

49.8%

SWE-bench

High is better

+53%69.1%

SWE-benchHigh is better

45.1%

GPQA

High is better

+29%84.6%

GPQAHigh is better

65.7%

TTFT

Low is better

195 ms

TTFTLow is better

160 ms+22%

Speed

High is better

118 tok/s

SpeedHigh is better

140 tok/s+19%

In $

Low is better

$2/1M

In $Low is better

$0.1/1M+1900%

Out $

Low is better

$6/1M

Out $Low is better

$0.32/1M+1775%

Context

High is better

+281%500k

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 95% 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)

Grok 4.6$160.00 / mo

In: $70.00Out: $90.00

Llama 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

Estimated Cost Delta

Llama 3.3 70B is estimated to save $151.70/month ($1,820/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Grok 4.6:Pick Grok 4.6 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 agentsGrok 4.6

Higher SWE-bench (69.1%).

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 RAGGrok 4.6

Larger window (500k).

Screenshots / visionGrok 4.6

Grok 4.6 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Grok 4.6	Higher SWE-bench (69.1%).
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	Grok 4.6	Larger window (500k).
Screenshots / vision	Grok 4.6	Grok 4.6 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

compareAug 12, 2026

Grok vs ChatGPT: price and Elo, not a personality contest

Brand hub remaps to top xAI vs top OpenAI. After Aug 12 that is Grok 4.6 vs Sol.

launchAug 12, 2026

Grok 4.6 (Aug 12): post-training refresh, same $2/$6 list

xAI’s Aug 12 2026 refresh of Grok 4.5. 500k context. The Grok vs ChatGPT hub remaps here.

launchJul 10, 2025

Grok 4 remains the previous xAI flagship on this catalog

2025-07 row. 4.6 is the buy. We keep 4 so upgrade pairs and old inbound links resolve.

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

xAI

Grok 4.6

Elo 1,592

LiveBench71.4%

SWE-bench69.1%

Speed118 tok/s

Output cost$6/1M

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, Grok 4.6 or Llama 3.3 70B?: Grok 4.6 has the higher preference Elo in our latest snapshot (1,592). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Grok 4.6 or Llama 3.3 70B?: Grok 4.6 leads SWE-bench at 69.1% 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 $6/1M. Input prices and retry rates still move the real bill.
Which is faster, Grok 4.6 or Llama 3.3 70B?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 195 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Grok 4.6 accepts 500k 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. Grok 4.6 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 Grok 4.6 matchups →|All Llama 3.3 70B matchups →

Grok 4.6 vs Grok 4 (previous grok)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 Grok 4.6 Claude Opus 4.8 vs Grok 4.6 GPT-5.6 Sol vs Grok 4.6

Grok 4.6 vs Llama 3.3 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Grok 4.6 (6)vsLlama 3.3 70B (4)

Grok 4.6: 6W (60%)Overall: Grok 4.6Llama 3.3 70B: 4W (40%)

← Grok 4.6Llama 3.3 70B →

Grok 4.6 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,592vs1,285

Coding Elo

1,568vs—

SWE-bench

69.1%vs45.1%

LiveBench

71.4%vs49.8%

GPQA Diamond

84.6%vs65.7%

Grok 4.6: 5WLlama 3.3 70B: 0W

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

195 msvs160 ms

Grok 4.6: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$6/1Mvs$0.32/1M

Input price

$2/1Mvs$0.1/1M

Context window

500kvs131k

Grok 4.6: 1WLlama 3.3 70B: 2W

Recommended Workload Routing

Repo / coding agentsGrok 4.6

Higher SWE-bench (69.1%).

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 RAGGrok 4.6

Larger window (500k).

Screenshots / visionGrok 4.6

Grok 4.6 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Grok 4.6	Higher SWE-bench (69.1%).
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	Grok 4.6	Larger window (500k).
Screenshots / vision	Grok 4.6	Grok 4.6 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Grok 4.6 or Llama 3.3 70B?

Grok 4.6 has the higher preference Elo in our latest snapshot (1,592). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Grok 4.6 or Llama 3.3 70B?

Grok 4.6 leads SWE-bench at 69.1% 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 $6/1M. Input prices and retry rates still move the real bill.

Which is faster, Grok 4.6 or Llama 3.3 70B?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 195 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Grok 4.6 accepts 500k 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. Grok 4.6 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

Grok 4.6

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

Grok vs ChatGPT: price and Elo, not a personality contest

Grok 4.6 (Aug 12): post-training refresh, same $2/$6 list

Grok 4 remains the previous xAI flagship on this catalog

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

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Grok 4.6

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

Grok 4.6

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

Grok vs ChatGPT: price and Elo, not a personality contest

Grok 4.6 (Aug 12): post-training refresh, same $2/$6 list

Grok 4 remains the previous xAI flagship on this catalog

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Grok 4.6

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons