Llama 3.1 8B Instruct model profile
Evidence snapshot:
Llama 3.1 8B Instruct is a Meta model profile with a 131,072-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, structured output, text generation, tool calling.
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 leading closed-source models in human evaluations. To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.
Llama 3.1 8B Instruct is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Llama 3.1 8B Instruct is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
Llama 3.1 8B Instruct accepts text and returns text. The snapshot records prompt caching, structured output, text generation, and tool calling as capabilities or interfaces.
The dated reference snapshot lists $0.02 input and $0.04 output per million tokens. Cached input is $0.025 per million tokens.
The 2026-09-06 snapshot includes 1 Artificial Analysis index and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Llama 3.1 8B Instruct ranked #79 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- Meta
- Context window
- 131,072 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- 2023-12-31
- Reference catalog ID
- meta-llama/llama-3.1-8b-instruct
When to pick Llama 3.1 8B Instruct
Llama 3.1 8B Instruct is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when cost is the binding constraint. At $0.02 in and $0.04 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the prompt is long. The 131K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.
Context, modalities, and identity
Llama 3.1 8B Instruct accepts text and returns text. The snapshot records prompt caching, structured output, text generation, and tool calling as capabilities or interfaces.
- Provider or publisher
- Meta
- Context window
- 131,072 tokens
- Maximum output
- 117,964 tokens
- Input modalities
- text
- Output modalities
- text
- Knowledge cutoff
- 2023-12-31
Model overview
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 leading closed-source models in human evaluations. To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.
- Reference model ID
- meta-llama/llama-3.1-8b-instruct
- Canonical version
- meta-llama/llama-3.1-8b-instruct
- Hugging Face ID
- meta-llama/Meta-Llama-3.1-8B-Instruct
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- frequency_penalty, logit_bias, logprobs, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
- prompt caching
- structured output
- text generation
- tool calling
Dated pricing snapshot
The dated reference snapshot lists $0.02 input and $0.04 output per million tokens. Cached input is $0.025 per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.02 per 1M tokens
- Cached input
- $0.025 per 1M tokens
- Output
- $0.04 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 5 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 5
- Best p50 latency
- 0.23 s
- Best p50 throughput
- 107 tok/s
- Availability with routing
- 99.98% over the sampled window
- Availability without routing
- 96.13% over the sampled window
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| DeepInfra | fp8 | $0.02 / 1M | $0.04 / 1M | No verified rate | 131,072 tokens | 0.77 s | 11 tok/s | 98.67% |
| NovitaAI | fp8 | $0.02 / 1M | $0.05 / 1M | No verified rate | 16,384 tokens | 0.48 s | 66 tok/s | 96.51% |
| Groq | unknown | $0.05 / 1M | $0.08 / 1M | $0.025 / 1M | 131,072 tokens | 0.23 s | 107 tok/s | 99.93% |
| Cloudflare | fp8 | $0.152 / 1M | $0.287 / 1M | No verified rate | 32,000 tokens | 0.70 s | 15 tok/s | 81.83% |
| CoreWeave | bf16 | $0.22 / 1M | $0.22 / 1M | $0.22 / 1M | 131,072 tokens | 0.28 s | 99 tok/s | 99.97% |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 1 Artificial Analysis index and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
- Coding Index
- 5.4
- AutoExacto coverage
- 6 provider observations over 32 days
AutoExacto provider benchmarks
Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.
| Provider | GPQA Diamond | TAU-Bench Airline | Runs |
|---|---|---|---|
| auto-routing | 29.62% | 19% | 4 |
| Cloudflare | 18.65% | — | 4 |
| CoreWeave | 27.11% | 0% | 4 |
| DeepInfra | 27.22% | — | 4 |
| Groq | 27.58% | — | 4 |
| NovitaAI | 25.68% | — | 4 |
Popularity and market context
Llama 3.1 8B Instruct ranked #79 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.
- Third-party catalog rank
- #79
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is a reference profile: Meta publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when cost is the binding constraint: At $0.02 in and $0.04 out per 1M tokens, combined token price sits in the cheapest quarter of the 146 models publishing both rates in this snapshot. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the prompt is long: The 131K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.
What is Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct is a Meta model profile with a 131,072-token context window. The dated catalog snapshot records text modalities and interfaces for prompt caching, structured output, text generation, tool calling.
What context window does Llama 3.1 8B Instruct have?
The dated profile lists 131,072 tokens of context and up to 117,964 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Llama 3.1 8B Instruct?
The 2026-09-06 snapshot includes 1 Artificial Analysis index and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is Llama 3.1 8B Instruct available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent Llama 3.1 8B Instruct's global market share?
Llama 3.1 8B Instruct ranked #79 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- Llama 3.1 8B Instruct third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- Meta official model documentation (primary, checked 2026-09-06)
- Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)
- Llama 3.1 8B Instruct third-party catalog record (benchmark, checked 2026-09-06)