Meta model product
1 70B is a large language model optimized for multilingual dialogue use cases.
Updated Sep 8, 2026. Default version: Llama 3.1 70B Instruct
This profile uses the model's current scored version. Arena ratings and prices are shown separately.
Benchmark scores for Llama 3.1 70B Instruct.
Benchmark | Score | Rank | Participants | Percentile | Evidence | Evaluated |
|---|
| BenchmarkGSM-8K (CoT) | Score95.10% | Rank01 | Participants2 | Percentile100.00% | EvidenceC | Evaluated |
| BenchmarkMATH (CoT) | Score68.00% | Rank01 | Participants6 | Percentile100.00% | EvidenceC | Evaluated |
| BenchmarkMBPP ++ base version | Score86.00% | Rank01 | Participants1 | Percentile100.00% | EvidenceC | Evaluated |
| BenchmarkAPI-Bank | Score90.00% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkBFCL | Score84.80% | Rank02 | Participants11 | Percentile90.00% | EvidenceC | Evaluated |
| BenchmarkGorilla Benchmark API Bench | Score29.70% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkMMLU (CoT) | Score86.00% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkMultilingual MGSM (CoT) | Score86.90% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkMultipl-E HumanEval | Score65.50% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkMultipl-E MBPP | Score62.00% | Rank02 | Participants3 | Percentile50.00% | EvidenceC | Evaluated |
| BenchmarkNexus | Score56.70% | Rank02 | Participants4 | Percentile66.67% | EvidenceC | Evaluated |
| BenchmarkARC-C | Score94.80% | Rank04 | Participants34 | Percentile90.91% | EvidenceC | Evaluated |
| BenchmarkDROP | Score79.60% | Rank14 | Participants30 | Percentile55.17% | EvidenceC | Evaluated |
| BenchmarkIFEval | Score87.50% | Rank33 | Participants68 | Percentile52.24% | EvidenceC | Evaluated |
| BenchmarkHumanEval | Score80.50% | Rank45 | Participants66 | Percentile32.31% | EvidenceC | Evaluated |
| BenchmarkMMLU | Score83.60% | Rank47 | Participants101 | Percentile54.00% | EvidenceC | Evaluated |
| BenchmarkMMLU-Pro | Score66.40% | Rank106 | Participants138 | Percentile23.36% | EvidenceC | Evaluated |
| BenchmarkLM Arena Text | Score1,260.68 rating | Rank184 | Participants210 | Percentile12.44% | EvidenceA | Evaluated |
| BenchmarkLM Arena Text Style Control | Score1,293.03 rating | Rank185 | Participants210 | Percentile11.96% | EvidenceA | Evaluated |
| BenchmarkGPQA | Score41.70% | Rank214 | Participants247 | Percentile13.41% | EvidenceC | Evaluated |
Preference and agent-evaluation results for the default version.
Arena | Category | Rank | Rating / score | Votes | Observations | Result date |
|---|
| Arenatext | Categoryfrench | Rank159 | Rating / score1,260.91 | Votes501 | ObservationsN/A | Result date |
| Arenatext | Categoryjapanese | Rank161 | Rating / score1,132.42 | Votes1,428 | ObservationsN/A | Result date |
| Arenatext | Categoryspanish | Rank161 | Rating / score1,252.30 | Votes606 | ObservationsN/A | Result date |
| Arenatext style control | Categoryfrench | Rank161 | Rating / score1,300.23 | Votes501 | ObservationsN/A | Result date |
| Arenatext | Categorygerman | Rank163 | Rating / score1,221.42 | Votes1,353 | ObservationsN/A | Result date |
| Arenatext | Categorykorean | Rank163 | Rating / score1,140.12 | Votes873 | ObservationsN/A | Result date |
| Arenatext style control | Categoryjapanese | Rank163 | Rating / score1,175.02 | Votes1,428 | ObservationsN/A | Result date |
| Arenatext style control | Categoryspanish | Rank163 | Rating / score1,287.07 | Votes606 | ObservationsN/A | Result date |
| Arenatext style control | Categorygerman | Rank166 | Rating / score1,255.74 | Votes1,353 | ObservationsN/A | Result date |
| Arenatext style control | Categorykorean | Rank166 | Rating / score1,187.09 | Votes873 | ObservationsN/A | Result date |
| Arenatext style control | Categoryindustry legal and government | Rank170 | Rating / score1,328.56 | Votes3,177 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry legal and government | Rank171 | Rating / score1,283.50 | Votes3,177 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry medicine and healthcare | Rank174 | Rating / score1,256.48 | Votes2,821 | ObservationsN/A | Result date |
| Arenatext | Categorymath | Rank174 | Rating / score1,251.65 | Votes7,677 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry life and physical and social science | Rank175 | Rating / score1,276.01 | Votes9,381 | ObservationsN/A | Result date |
| Arenatext | Categoryenglish | Rank177 | Rating / score1,293.62 | Votes29,694 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry mathematical | Rank177 | Rating / score1,253.44 | Votes6,524 | ObservationsN/A | Result date |
| Arenatext style control | Categoryindustry life and physical and social science | Rank178 | Rating / score1,318.36 | Votes9,381 | ObservationsN/A | Result date |
| Arenatext style control | Categorymath | Rank178 | Rating / score1,269.11 | Votes7,677 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry business and management and financial operations | Rank179 | Rating / score1,234.65 | Votes6,381 | ObservationsN/A | Result date |
| Arenatext style control | Categoryindustry mathematical | Rank179 | Rating / score1,275.20 | Votes6,524 | ObservationsN/A | Result date |
| Arenatext style control | Categoryindustry medicine and healthcare | Rank179 | Rating / score1,312.33 | Votes2,821 | ObservationsN/A | Result date |
| Arenatext style control | Categoryenglish | Rank180 | Rating / score1,320.93 | Votes29,694 | ObservationsN/A | Result date |
| Arenatext | Categoryindustry entertainment and sports and media | Rank182 | Rating / score1,229.71 | Votes8,719 | ObservationsN/A | Result date |
| Arenatext style control | Categorycoding | Rank182 | Rating / score1,333.23 | Votes9,389 | ObservationsN/A | Result date |
| Arenatext style control | Categoryindustry entertainment and sports and media | Rank182 | Rating / score1,263.31 | Votes8,719 | ObservationsN/A | Result date |
| Arenatext style control | Categorymulti turn | Rank182 | Rating / score1,288.63 | Votes10,405 | ObservationsN/A | Result date |
| Arenatext | Categoryexclude ties | Rank183 | Rating / score1,176.39 | Votes34,840 | ObservationsN/A | Result date |
| Arenatext style control | Categorychinese | Rank183 | Rating / score1,277.40 | Votes4,846 | ObservationsN/A | Result date |
| Arenatext style control | Categorycreative writing | Rank183 | Rating / score1,256.63 | Votes8,250 | ObservationsN/A | Result date |
Official vendor API pricing appears first, followed by individual provider offers.
Provider | Provider model ID | Region | Input / 1M | Output / 1M | Context | Updated |
|---|
| ProviderNvidia | Provider model IDmeta/llama-3.1-70b-instruct | Regionglobal | Input / 1MN/A | Output / 1MN/A | Context128K | Updated |
| ProviderOpenRouter | Provider model IDmeta-llama/llama-3.1-70b-instruct | Regionglobal | Input / 1M$0.40 | Output / 1M$0.40 | Context131.1K | Updated |
| ProviderAmazon Bedrock | Provider model IDmeta.llama3-1-70b-instruct-v1:0 | Regionglobal | Input / 1M$0.72 | Output / 1M$0.72 | Context128K | Updated |
| ProviderVercel AI Gateway | Provider model IDmeta/llama-3.1-70b | Regionglobal | Input / 1M$0.72 | Output / 1M$0.72 | Context128K | Updated |
| ProviderDevPass (LLM Gateway) | Provider model IDllama-3.1-70b-instruct | Regionglobal | Input / 1M$0.72 | Output / 1M$0.72 | Context128K | Updated |
Official prices use only the vendor's configured official Provider and positive standard USD PAYG rates. Third-party offers remain explicitly labeled.
Provider-specific output speed and catalog latency for Llama 3.1 70B Instruct. Runtime does not affect the capability score.
Provider | Output Speed | Catalog Latency | Max Input | Max Output | Updated |
|---|
| ProviderCerebras | Output Speed1,204.00 tok/s | Catalog Latency0.20 s | Max Input128K | Max Output128K | Updated |
| ProviderGroq | Output Speed250.00 tok/s | Catalog Latency0.50 s | Max Input128K | Max Output128K | Updated |
| ProviderDeepInfra | Output Speed25.00 tok/s | Catalog Latency0.50 s | Max Input128K | Max Output128K | Updated |
Output Speed is generated output tokens received per second. Catalog latency is reported separately from observed provider TTFT.
Technical details for the model's default version.
Available versions of this model. The score column identifies the version used in the overall ranking.
Version | Released | LLMBoard | Parameters | Context | Max output | Open weights | License |
|---|
| VersionLlama 3.1 70B Instruct | Released | LLMBoard11.47 | Parameters70B | Context128K | Max output128K | Open weightsYes | LicenseLlama 3.1 Community License |
Recommendations prioritize the same model type and family, then the closest LLMBoard score.
Key information about Llama 3.1 70B and its available data.
1 70B Instruct is a large language model from Meta optimized for multilingual dialogue use cases. It outperforms many available open source and closed chat models on common industry benchmarks.
Data as of 2026-09-08.
Common questions about Llama 3.1 70B.
Llama 3.1 70B's default version was released on Jul 23, 2024.
No official standard PAYG price is currently available for Llama 3.1 70B. The lowest tracked third-party offer starts at $0.40 input and $0.40 output via OpenRouter.
Llama 3.1 70B was created by Meta.
The default version has a 128K token context window.
Yes. The default version is marked as open weight under Llama 3.1 Community License.
5 provider offerings are linked to the default version.