meta-llama
Meta: Llama 3.1 8B Instruct
meta-llama/llama-3.1-8b-instruct
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to its OpenRouter catalog entry, meta-llama/llama-3.1-8b-instruct is an 8-billion-parameter instruction-tuned text model from Meta, part of the Llama 3.1 family, which the catalog describes as fast and efficient. It is listed with a long context window, a knowledge cutoff at the end of 2023, and support for tool calling and structured output parameters. Measured data is thin: only a single coding-oriented benchmark figure is reported, and an independent model card was not available in this source pack, so claims rest on catalog information alone.[1]
Strengths
Limitations
Where to use it
- May suit high-volume chat or instruction-following workloads where a small instruct model suffices; validate on your own data.[1]
- A possible fit for tool-use or structured-output pipelines, given the listed parameters.[1]
- The listed context window suggests exploring long-document summarization or QA, subject to testing.[1]
Sources
- [1] OpenRouter catalog · Meta: Llama 3.1 8B InstructSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...
Specifications
- Model ID
- meta-llama/llama-3.1-8b-instruct
- Creator
- meta-llama
- Added to OpenRouter
- 23.07.2024
- Context length
- 131,072
- Modality
- text->text
- Input modalities
- text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.020 / out $0.040
- Knowledge cutoff
- 31.12.2023
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | CC — below the B band | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 5.4 | CC — below the B band | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| DeepInfrafp8 | 131,072 | $0.020 | $0.040 | — | — | 100% |
| Groq | 131,072 | $0.050 | $0.080 | — | — | 100% |
| Cloudflarefp8 | 32,000 | $0.152 | $0.287 | — | — | 100% |
| CoreWeavebf16 | 131,072 | $0.220 | $0.220 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.