Meta, open weights

# Llama-3.3-70B-Instruct

> Llama-3.3-70B-Instruct by Meta, released December 2024. Ranked #291 of 354 with a Noometry Index of 30.6. API: $0.10 in / $0.32 out per M tokens. 128K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Title: Llama-3.3-70B-Instruct Benchmarks, Price & Rank (October 2026)

Llama-3.3-70B-Instruct by Meta ranks 291st of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.6. Its strongest category is agentic & tool use, where it ranks 105th. API pricing starts at $0.10 per million input tokens and $0.32 per million output tokens, with a 128K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #291 of 354
- **Index score:** 30.6
- **Evidence:** Confirmed 43 results
- **Provider:** [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta)
- **Released:** December 6, 2024
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 128K
- **Max output:** 4K
- **Input price:** $0.10 / M
- **Output price:** $0.32 / M
- **Blended price:** $0.16 / M
- **Output speed:** Not measured
- **Value:** #43 of 219
- **Knowledge cutoff:** December 2023
- **Input:** text
- **Hugging Face:** [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)

## Category scores

Each category score combines every public result we have in that category.

Llama-3.3-70B-Instruct category scores

1.  Coding 31.0
2.  Agentic & Tool Use 25.8
3.  Reasoning 14.1
4.  Math 15.3
5.  Knowledge 30.6
6.  Multilingual 39.9
7.  Instruction Following 71.1
8.  Long Context 26.4
9.  Writing & Preference 47.6
10.  020406080

Llama-3.3-70B-Instruct category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 31.0 | #290 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.8 | #105 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 14.1 | #327 | 7 |
| [Math](https://noometry.com/best/math) | 15.3 | #298 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 30.6 | #226 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 39.9 | #220 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 71.1 | #157 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 26.4 | #295 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 47.6 | #207 | 4 |

## Strengths and weaknesses

Categories where Llama-3.3-70B-Instruct places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Llama-3.3-70B-Instruct: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 71.1 | −0.2 | #157 of 305, top 52% |
| [Writing & Preference](https://noometry.com/best/writing) | 47.6 | −6.2 | #207 of 312, top 67% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 25.8 | −4.5 | #105 of 154, top 69% |

### Weakest categories

Llama-3.3-70B-Instruct: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 26.4 | −14.5 | #295 of 296, top 100% |
| [Reasoning](https://noometry.com/best/reasoning) | 14.1 | −9.5 | #327 of 350, top 94% |
| [Math](https://noometry.com/best/math) | 15.3 | −21.3 | #298 of 327, top 92% |

## Closest competitors

The models ranked just above and below Llama-3.3-70B-Instruct. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Llama-3.3-70B-Instruct
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Codellama 34b Instruct](https://noometry.com/models/codellama-34b-instruct) | #287 | 30.8 | — | — | [Compare](https://noometry.com/compare/codellama-34b-instruct-vs-llama-3-3-70b-instruct) |
| [Llama 3.1-405B](https://noometry.com/models/llama-3-1-405b) | #288 | 30.7 | — | 78 | [Compare](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-3-70b-instruct) |
| [Yi-1.5-34B](https://noometry.com/models/yi-1-5-34b) | #289 | 30.6 | — | — | [Compare](https://noometry.com/compare/llama-3-3-70b-instruct-vs-yi-1-5-34b) |
| [Codestral](https://noometry.com/models/codestral) | #290 | 30.6 | $0.45 | 271 | [Compare](https://noometry.com/compare/codestral-vs-llama-3-3-70b-instruct) |
| [GPT-4 Turbo](https://noometry.com/models/gpt-4-turbo) | #292 | 30.5 | $15 | — | [Compare](https://noometry.com/compare/gpt-4-turbo-vs-llama-3-3-70b-instruct) |
| [Qwen1.5-32B](https://noometry.com/models/qwen1-5-32b) | #293 | 30.5 | — | — | [Compare](https://noometry.com/compare/llama-3-3-70b-instruct-vs-qwen1-5-32b) |
| [Amazon Nova Micro](https://noometry.com/models/amazon-nova-micro) | #294 | 30.4 | $0.0613 | — | [Compare](https://noometry.com/compare/amazon-nova-micro-vs-llama-3-3-70b-instruct) |
| [Olmo 7b Instruct](https://noometry.com/models/olmo-7b-instruct) | #295 | 30.3 | — | — | [Compare](https://noometry.com/compare/llama-3-3-70b-instruct-vs-olmo-7b-instruct) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Llama-3.3-70B-Instruct Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SciCode](https://noometry.com/benchmarks/scicode) | 26% | #111 of 121, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 14.4% | #109 of 119, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 46.9% | #10 of 64, top 16% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-12-19 |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 36.6% | #26 of 39, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1268 | #219 of 294, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 57.5% | #12 of 66, top 19% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-12-19 |

### Agentic & Tool Use

Llama-3.3-70B-Instruct Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 31.9% | #32 of 49, top 66% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 23% | #22 of 35, top 63% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Llama-3.3-70B-Instruct Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 19.9% | #73 of 77, top 95% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #117 of 134, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 50.8% | #19 of 39, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1257 | #214 of 297, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 59.5% | #118 of 151, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 49.5% | #25 of 39, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 17.5% | #97 of 125, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 127.33 | #148 of 213, top 70% |  | [Epoch AI](https://epoch.ai/eci) | 2024-12-06 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 58.6 | #45 of 72, top 63% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 50.2% | #19 of 39, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Llama-3.3-70B-Instruct Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 5.1% | #147 of 173, top 85% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 42.2% | #24 of 39, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1267 | #206 of 285, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 41.6% | #51 of 79, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |

### Knowledge

Llama-3.3-70B-Instruct Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 47.4% | #135 of 186, top 73% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 22.8% | #40 of 51, top 79% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 4.1% | #4 of 96, top 5% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1225 | #209 of 273, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 86.3% | #6 of 81, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Llama-3.3-70B-Instruct Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1236 | #220 of 297, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1217 | #224 of 285, top 79% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1281 | #170 of 223, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1251 | #177 of 231, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1150 | #175 of 211, top 83% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1143 | #180 of 213, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1252 | #212 of 283, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1270 | #173 of 226, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Llama-3.3-70B-Instruct Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 82.7% | #5 of 39, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1242 | #219 of 298, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Llama-3.3-70B-Instruct Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 33.3% | #46 of 47, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1256 | #217 of 291, top 75% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Llama-3.3-70B-Instruct Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1274 | #213 of 297, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1250 | #204 of 295, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1280 | #201 of 295, top 69% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 39.2% | #20 of 39, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

Llama-3.3-70B-Instruct API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.71 | $0.71 | — | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.72 | $0.72 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.10 | $0.32 | — | 2026-10-10 |
| [groq](https://console.groq.com/docs/models) | $0.59 | $0.79 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/meta-llama/llama-3.3-70b-instruct) | $0.22 | $0.50 | $0.11 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $1.04 | $1.04 | — | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $0.72 | $0.72 | — | 2026-10-10 |

[All Meta API prices →](https://noometry.com/llm-pricing/meta) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Llama-3.3-70B-Instruct

-   [Llama-3.3-70B-Instruct vs Codestral](https://noometry.com/compare/codestral-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs GPT-4 Turbo](https://noometry.com/compare/gpt-4-turbo-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Yi-1.5-34B](https://noometry.com/compare/llama-3-3-70b-instruct-vs-yi-1-5-34b)
-   [Llama-3.3-70B-Instruct vs Qwen1.5-32B](https://noometry.com/compare/llama-3-3-70b-instruct-vs-qwen1-5-32b)
-   [Llama-3.3-70B-Instruct vs Llama 3.1-405B](https://noometry.com/compare/llama-3-1-405b-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Amazon Nova Micro](https://noometry.com/compare/amazon-nova-micro-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs Qwen3.8 Max](https://noometry.com/compare/llama-3-3-70b-instruct-vs-qwen3-8-max)
-   [Llama-3.3-70B-Instruct vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-llama-3-3-70b-instruct)
-   [Llama-3.3-70B-Instruct vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-llama-3-3-70b-instruct)

## Other Meta models

-   [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3)54.8
-   [Muse Spark](https://noometry.com/models/muse-spark)50.6
-   [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2)50.3
-   [Muse Spark 1.1](https://noometry.com/models/muse-spark-1-1)49.9
-   [Muse Glimmer](https://noometry.com/models/muse-glimmer)41.7
-   [Codellama 70b Instruct](https://noometry.com/models/codellama-70b-instruct)33.7
-   [Llama 4 Maverick](https://noometry.com/models/llama-4-maverick)30.9
-   [Codellama 34b Instruct](https://noometry.com/models/codellama-34b-instruct)30.8

## Frequently asked questions

### How good is Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct by Meta ranks 291st of 354 ranked models on the Noometry Index as of October 2026, with a score of 30.6. Its strongest category is agentic & tool use, where it ranks 105th. API pricing starts at $0.10 per million input tokens and $0.32 per million output tokens, with a 128K-token context window.

### How much does Llama-3.3-70B-Instruct cost?

Llama-3.3-70B-Instruct costs $0.10 per million input tokens and $0.32 per million output tokens on deepinfra.

### What is Llama-3.3-70B-Instruct's context window?

Llama-3.3-70B-Instruct accepts up to 128K tokens of input and can write up to 4K tokens in one response.

### Is Llama-3.3-70B-Instruct open source?

Yes. Llama-3.3-70B-Instruct's weights are downloadable from Hugging Face (meta-llama/Llama-3.3-70B-Instruct); check the license for commercial terms.

### What are Llama-3.3-70B-Instruct's strengths and weaknesses?

Relative to other ranked models, Llama-3.3-70B-Instruct places best in instruction following, writing & preference, agentic & tool use and lowest in long context, reasoning, math.

### What is Llama-3.3-70B-Instruct best at?

Its best category is agentic & tool use, where it ranks 105th on Noometry.

### Cite this page

Noometry. (2026). Llama-3.3-70B-Instruct benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/llama-3-3-70b-instruct

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/llama-3-3-70b-instruct.md).