Llama 4 Scout API model profile
Evidence snapshot:
Llama 4 Scout is a Meta model profile with a 1,310,720-token context window. The dated catalog snapshot records text, image modalities and interfaces for long context, structured output, text generation, tool calling, tools.
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Llama 4 Scout is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.
Llama 4 Scout is available under the Kendr alias kc-llama-4-scout. Current account availability and customer credit quotes come from Kendr's live public model API and applicable account policy.
Llama 4 Scout accepts text and image and returns text. The snapshot records long context, structured output, text generation, tool calling, tools, vision, and web search as capabilities or interfaces.
The dated reference snapshot lists $0.1 input and $0.3 output per million tokens. The separately published provider-route reference is $0.17 / $0.66.
The 2026-09-06 snapshot includes 1 Artificial Analysis index and 5 Design Arena categories. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
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
- Kendr API model
- Kendr API alias
- kc-llama-4-scout
- Model developer
- Meta
- Kendr route provider
- Meta
- Context window
- 3.5M
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- 2024-08-31
- Reference catalog ID
- meta-llama/llama-4-scout
When to pick Llama 4 Scout
Llama 4 Scout is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.
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 a whole corpus has to fit in one prompt. The 1.31M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked.
- Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
- 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.
Context, modalities, and identity
Llama 4 Scout accepts text and image and returns text. The snapshot records long context, structured output, text generation, tool calling, tools, vision, and web search as capabilities or interfaces.
- Model developer
- Meta
- Kendr route provider
- Meta
- Context window
- 3.5M
- Maximum output
- 16,384 tokens
- Input modalities
- text and image
- Output modalities
- text
- Knowledge cutoff
- 2024-08-31
Model overview
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
- Reference model ID
- meta-llama/llama-4-scout
- Canonical version
- meta-llama/llama-4-scout-17b-16e-instruct
- Hugging Face ID
- meta-llama/Llama-4-Scout-17B-16E-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, max_tokens, min_p, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_p
- long context
- structured output
- text generation
- tool calling
- tools
- vision
- web search
Dated pricing snapshot
The dated reference snapshot lists $0.1 input and $0.3 output per million tokens. The separately published provider-route reference is $0.17 / $0.66.
Reference figures are dated 2026-09-06; the live Kendr quote can differ by selected provider route, context tier, caching, tools, region, and current rate card.
- Price date
- 2026-09-06
- Input
- $0.1 per 1M tokens
- Cached input
- No verified rate
- Output
- $0.3 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 3 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 3
- Best p50 latency
- No recent metric
- Best p50 throughput
- No recent metric
- Availability with routing
- No recent metric
- Availability without routing
- No recent metric
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| DeepInfra | fp8 | $0.1 / 1M | $0.3 / 1M | No verified rate | 327,680 tokens | No recent p50 | No recent p50 | 99.95% |
| Novita | bf16 | $0.18 / 1M | $0.59 / 1M | No verified rate | 131,072 tokens | No recent p50 | No recent p50 | 99.79% |
| unknown | $0.25 / 1M | $0.7 / 1M | No verified rate | 1,310,720 tokens | No recent p50 | No recent p50 | 99.94% |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 1 Artificial Analysis index and 5 Design Arena categories. 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
- 8.2
| Design Arena category | Elo | Win rate | Rank |
|---|---|---|---|
| codecategories | 806 | 26.6% | #123 |
| dataviz | 913 | 39.3% | #114 |
| gamedev | 802 | 27.4% | #122 |
| uicomponent | 790 | 25.5% | #117 |
| website | 763 | 22.7% | #129 |
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
OpenAI-compatible API example
This example applies to the published Kendr alias on this hosted profile. Check the live public catalog before use.
curl https://api.kendr.org/v1/chat/completions \
-H "Authorization: Bearer $KENDR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"kc-llama-4-scout","messages":[{"role":"user","content":"Hello"}]}'Frequently asked questions
When should I use Llama 4 Scout?
Llama 4 Scout is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support. Reach for it when a whole corpus has to fit in one prompt: The 1.31M-token context window is in the top quarter of the 163 models publishing a limit here, so long documents can go in whole rather than chunked. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. 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.
What is Llama 4 Scout?
Llama 4 Scout is a Meta model profile with a 1,310,720-token context window. The dated catalog snapshot records text, image modalities and interfaces for long context, structured output, text generation, tool calling, tools.
What context window does Llama 4 Scout have?
The dated profile lists 3.5M of context and up to 16,384 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for Llama 4 Scout?
The 2026-09-06 snapshot includes 1 Artificial Analysis index and 5 Design Arena categories. 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.
What is the Kendr API alias for Llama 4 Scout?
Use kc-llama-4-scout as the model value. The live public model API and signed-in catalog remain authoritative for route availability and customer credit quotes.
How is Llama 4 Scout priced on Kendr?
The dated reference snapshot lists $0.1 input and $0.3 output per million tokens. The separately published provider-route reference is $0.17 / $0.66. Kendr applies one 5% markup to configured provider model cost; the live quote and settled routing receipt are authoritative for a request.
Sources and evidence dates
- Llama 4 Scout 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)
- Design Arena leaderboard and methodology (benchmark, checked 2026-09-06)
- Llama 4 Scout provider or route reference (primary, checked 2026-09-06)