Llama 4 Scout Developer Guide 2026: 10M Token Context Window for Full Codebase Analysis

Llama 4 Scout is Meta’s open-weight model with a 10 million token context window — the largest of any open-weight model released in 2026. At roughly 4 tokens per line of code, that covers approximately 2.5 million lines of code in a single prompt. In practice this means you can load an entire mid-size production repository — including tests, docs, and config — without chunking, vector databases, or retrieval pipelines.

What Is Llama 4 Scout? Model Specs and Architecture

Llama 4 Scout is a Mixture-of-Experts (MoE) model with 109 billion total parameters and 17 billion active parameters per forward pass, distributed across 16 experts. Meta trained it on 40 trillion tokens of data — roughly 3× the dataset used for Llama 3. The model fits on a single NVIDIA H100 80GB GPU using INT4 AWQ quantization (approximately 55GB), which makes it unusual for its class: a 10M context model that doesn’t require a server cluster. The architecture uses iRoPE (interleaved Rotary Position Embeddings with NoPE layers), which applies temperature scaling at specific transformer layers to maintain coherence at extreme context lengths. This is not a marketing claim — Scout maintains greater than 99% accuracy at 10 million tokens in NIAH (Needle in a Haystack) long-context retrieval tests, meaning it reliably finds specific information buried anywhere in a 10M token prompt. For developers, the key architectural implication is that context degradation — the “lost in the middle” problem that plagues other models — is substantially reduced. Upstream on Hugging Face, the full model is meta-llama/Llama-4-Scout-17B-16E-Instruct.

Why 10M Tokens Is a Qualitative Shift for Developers

Ten million tokens is not just a bigger number — it changes the entire architecture of AI coding tools. Before Scout, any serious codebase analysis required building a RAG pipeline: chunking files, generating embeddings, storing vectors, writing retrieval logic, tuning similarity thresholds, and handling chunk-boundary artifacts where a function gets split across two retrieved chunks. With Scout’s 10M context window, that entire layer disappears. Load the repo directly, ask questions, get answers that span across the entire codebase simultaneously.

At $0.08 per million input tokens (the cheapest available provider), loading a 2M-token codebase costs $0.16 per analysis run. Compare this to the engineering time to build and maintain a RAG pipeline — even one day of engineering time is worth hundreds of thousands of RAG queries at Scout’s pricing. Beyond cost, there’s correctness: RAG systems miss cross-file relationships unless you’ve designed explicit cross-reference retrieval. Scout sees everything at once. I’ve found that for security audits, refactoring analysis, and onboarding large codebases, the “just load it all” approach catches issues that chunked retrieval consistently misses — especially when a bug involves three files that wouldn’t normally be retrieved together.

The Math on Codebase Coverage

At 4 tokens per line (a reasonable average for mixed Python/TypeScript/Go), 10M tokens covers approximately 2.5 million lines. A typical mid-size SaaS backend runs 200,000–500,000 lines of code. You can fit 5–12 of these simultaneously. The Linux kernel is 36 million lines — too large for one pass, but any single subsystem fits comfortably. For most teams, the entire active codebase fits in a single context with room to spare for full test suites and documentation.

Llama 4 Scout vs. GPT-4.1, Claude, and Gemini: Context Window Comparison

Llama 4 Scout occupies a unique position in the context window landscape: it matches Gemini 3 Pro’s 10M token maximum but costs 150× less per million input tokens. Here’s the current state of large-context models:

The cost angle is stark. For a 1M token codebase analysis job, Scout costs $0.08–$0.15. Claude Opus 4.5 costs $25 for the same prompt (even ignoring that it doesn’t support 1M context). That’s a 312× price difference for tasks where you specifically need large context. The caveat: providers cap Scout’s actual context window. At launch, Groq supports up to 2M tokens, Together AI supports up to 4M, and Fireworks handles up to the full 10M on dedicated instances. The 10M theoretical maximum is only available via direct deployment.

Provider Availability and Actual Context Limits

The gap between “model supports 10M tokens” and “you can actually use 10M tokens” is significant. Shared inference infrastructure has memory constraints — most providers implement hard caps. Check each provider’s current limits before designing workflows that depend on maximum context:

• Groq: Fast inference ($0.11/M input, $0.34/M output), capped at 2M tokens
• Together AI: Up to 4M context, competitive pricing
• Fireworks: Full 10M on dedicated instances, higher cost
• OpenRouter: Aggregates providers, Scout available via multiple backends

API Quickstart: Groq, Together AI, Fireworks, and OpenRouter

Scout exposes an OpenAI-compatible API across all major providers, meaning zero code changes if you’re switching from GPT-4. The model ID varies by provider, but the interface is identical. Groq is the fastest option for inference speed and the cheapest for output tokens at $0.34/M, though its context cap is 2M tokens. Together AI offers up to 4M context with slightly higher pricing. Fireworks is the only provider currently supporting Scout’s full 10M context window via dedicated instances, but at a premium price. OpenRouter aggregates all three, letting you switch backends without changing your code. For most developers starting out, Groq is the right first choice — fast, cheap, and the API is live immediately after signup. Switch to Together AI or Fireworks once you need context windows larger than 2M tokens. All providers support streaming responses via stream=True, which is important for large-context requests where first-token latency is 15–60 seconds — streaming lets you show progress rather than blocking on a full response. Authentication follows the standard Bearer token pattern, and rate limits at the free tier are sufficient for development and testing.

Python with Groq

from groq import Groq

client = Groq(api_key="your-groq-api-key")

with open("my_repo.txt", "r") as f:
    codebase = f.read()

response = client.chat.completions.create(
    model="meta-llama/llama-4-scout-17b-16e-instruct",
    messages=[
        {
            "role": "user",
            "content": f"Here is the full codebase:\n\n{codebase}\n\nFind all SQL injection vulnerabilities and explain the data flow for each one."
        }
    ],
    max_tokens=4096,
    temperature=0.1
)

print(response.choices[0].message.content)

Python with Together AI (higher context limits)

from openai import OpenAI

client = OpenAI(
    api_key="your-together-api-key",
    base_url="https://api.together.xyz/v1"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[
        {"role": "system", "content": "You are a senior software engineer performing codebase analysis."},
        {"role": "user", "content": codebase_content}
    ],
    max_tokens=8192
)

Shell/curl for quick tests

curl https://api.groq.com/openai/v1/chat/completions \
  -H "Authorization: Bearer $GROQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-4-scout-17b-16e-instruct",
    "messages": [{"role": "user", "content": "Analyze this code: ..."}],
    "max_tokens": 2048
  }'

Full Codebase Analysis Without RAG: Code Examples

The most useful capability Scout unlocks is loading a complete repository and asking questions that require understanding the full codebase simultaneously. Here’s a production-ready script for codebase loading:

import os
from pathlib import Path
from groq import Groq

SKIP_DIRS = {".git", "node_modules", "__pycache__", ".venv", "dist", "build"}
SKIP_EXTENSIONS = {".png", ".jpg", ".gif", ".ico", ".lock", ".bin", ".wasm"}

def load_codebase(repo_path: str, max_tokens: int = 1_500_000) -> str:
    """
    Loads a repository into a single string for Scout.
    Estimates tokens as len(text) / 4 (conservative).
    """
    files = []
    total_chars = 0
    char_limit = max_tokens * 4

    for path in sorted(Path(repo_path).rglob("*")):
        if path.is_file():
            if any(d in path.parts for d in SKIP_DIRS):
                continue
            if path.suffix.lower() in SKIP_EXTENSIONS:
                continue
            try:
                content = path.read_text(encoding="utf-8", errors="ignore")
                relative = path.relative_to(repo_path)
                entry = f"\n\n### FILE: {relative}\n\n{content}"
                if total_chars + len(entry) > char_limit:
                    break
                files.append(entry)
                total_chars += len(entry)
            except Exception:
                continue

    return "".join(files)


client = Groq(api_key=os.environ["GROQ_API_KEY"])

codebase = load_codebase("/path/to/your/repo")
estimated_tokens = len(codebase) // 4
print(f"Loaded ~{estimated_tokens:,} tokens")

response = client.chat.completions.create(
    model="meta-llama/llama-4-scout-17b-16e-instruct",
    messages=[
        {
            "role": "system",
            "content": "You are a senior engineer reviewing a production codebase. Be specific and cite file paths."
        },
        {
            "role": "user",
            "content": f"{codebase}\n\n---\n\nTask: Generate a complete architectural diagram description covering all service boundaries, data flows, and external dependencies. Then list the top 5 technical debt items with their file locations."
        }
    ],
    max_tokens=4096,
    temperature=0.1
)

print(response.choices[0].message.content)

This approach replaces a full RAG pipeline for repos under about 350,000 lines of code (at Groq’s 2M context cap). For full 10M context, use Fireworks or deploy locally.

Multimodal Capabilities: Combining Vision and Long-Context Text

Llama 4 Scout is natively multimodal — it processes images and text in the same context window. This is genuinely useful for codebase analysis when combined with architecture diagrams. In practice, I’ve used it to feed both a database ERD image and the full ORM layer code simultaneously, asking Scout to verify that the code correctly implements the relationships shown in the diagram. It catches discrepancies that pure code review misses because the diagram is the ground truth.

import base64

def encode_image(image_path: str) -> str:
    with open(image_path, "rb") as f:
        return base64.b64encode(f.read()).decode("utf-8")

arch_diagram = encode_image("architecture.png")

response = client.chat.completions.create(
    model="meta-llama/llama-4-scout-17b-16e-instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{arch_diagram}"
                    }
                },
                {
                    "type": "text",
                    "text": f"Here is the system architecture diagram. Now here is the full implementation:\n\n{codebase}\n\nIdentify where the implementation diverges from the architecture."
                }
            ]
        }
    ],
    max_tokens=4096
)

This multimodal + long-context combination is one of Scout’s strongest differentiators versus pure text models. No other open-weight model currently supports both 10M context and vision in the same request.

Local Deployment with Ollama and vLLM

Self-hosting Scout makes sense for teams with privacy requirements, regulated industries where code can’t leave the premises, or workloads with high volume where API costs add up. The tradeoff: hardware requirements are substantial. The Q4_K_M quantized model weighs approximately 60GB, requiring either a single NVIDIA H100 80GB GPU or two RTX 4090s with VRAM pooling. Full FP16 inference requires approximately 218GB, meaning a minimum of three H100 80GB GPUs. For most teams, this puts local deployment in the “dedicated inference server” category rather than developer laptop territory. Ollama is the easiest deployment path — one command to pull and one command to run. vLLM offers higher throughput, better multi-user support via continuous batching and PagedAttention, and OpenAI-compatible API endpoints out of the box. The key practical limitation for local deployment: KV cache memory. At 10M tokens, the KV cache alone requires hundreds of GB beyond the model weights, which is why even local deployments practically cap context at 128K–512K tokens unless you have a multi-GPU setup specifically configured for long context. For teams processing entire repositories overnight (batch jobs, CI pipelines), local vLLM deployment with 512K context covers most real-world repos and completely eliminates per-token API costs after the hardware investment.

Ollama Setup (Easiest Path)

# Pull the Q4_K_M quantized model (~60GB download)
ollama pull llama4:scout

# Run inference
ollama run llama4:scout "Analyze this code: ..."

# Or via API
curl http://localhost:11434/api/chat \
  -d '{
    "model": "llama4:scout",
    "messages": [{"role": "user", "content": "Your prompt here"}]
  }'

Hardware requirements for Ollama:

• Q4_K_M quantization: ~60GB VRAM or RAM
• Recommended: 2× NVIDIA RTX 4090 (48GB total) or 1× H100 80GB
• CPU-only inference is possible but very slow for large contexts (10+ minutes per request)

Important: Ollama’s default context window is 2048 tokens. For large-context use you must explicitly set it:

ollama run llama4:scout --ctx-size 131072  # 128K context

Ollama doesn’t currently support Scout’s full 10M context window locally — at 60GB for Q4_K_M weights, the KV cache for 10M tokens would require hundreds of additional GB of memory. Practical local context is 128K–512K tokens depending on hardware.

vLLM Setup (Production Self-Hosting)

For production self-hosting with higher throughput and full context support:

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --quantization awq \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.9 \
  --tensor-parallel-size 1

For full 10M context via vLLM, you need multiple H100s. The KV cache at 10M tokens in FP8 requires approximately 160GB additional GPU memory on top of the 55GB model weights — meaning a minimum of 3× H100 80GB for full 10M context in FP8. In practice, most teams using vLLM for Scout set --max-model-len to 500K–2M tokens, which is manageable on 2× H100.

from openai import OpenAI

# vLLM exposes OpenAI-compatible API
client = OpenAI(
    api_key="not-needed",
    base_url="http://localhost:8000/v1"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[{"role": "user", "content": your_prompt}],
    max_tokens=4096
)

Production Considerations: Latency, Memory, and Provider Limits

Before deploying Scout in production, understand the latency reality. At full 10M tokens, first-token latency (TTFT) exceeds 60 seconds even on top-tier hardware. This is physics, not a bug — processing 10M tokens through a 17B active parameter transformer takes time regardless of hardware. Here’s the practical latency breakdown by context size:

For production use cases, architect around Scout’s latency profile:

import asyncio
from groq import AsyncGroq

async def analyze_batch(codebases: list[str]) -> list[str]:
    """
    Run multiple codebase analyses concurrently.
    Scout's latency at large contexts means async/parallel is essential.
    """
    client = AsyncGroq(api_key=os.environ["GROQ_API_KEY"])

    tasks = [
        client.chat.completions.create(
            model="meta-llama/llama-4-scout-17b-16e-instruct",
            messages=[{"role": "user", "content": code}],
            max_tokens=2048
        )
        for code in codebases
    ]

    results = await asyncio.gather(*tasks)
    return [r.choices[0].message.content for r in results]

Scout fits well in nightly batch analysis jobs, CI/CD pipeline checks that run after merge (not blocking merge), and async security scanning. It doesn’t fit interactive developer tools where latency matters.

Real-World Use Cases Beyond Codebase Analysis

Scout’s 10M context window opens workflows beyond just “load the whole repo”:

Security audits: Load the full codebase, dependency list, and CVE database for the known libraries — all in one pass. Ask Scout to identify code paths that use vulnerable library functions. This cross-references three information sources simultaneously without any retrieval logic.

Legacy code migration: Load the full legacy Python 2 codebase plus the target Python 3 style guide plus 500 existing migration examples. Ask Scout to generate migration patches that match your team’s established patterns — it has enough context to maintain consistency across the entire migration.

API contract verification: Load all client SDKs, backend handler implementations, and OpenAPI spec simultaneously. Ask Scout to identify every place where client expectations diverge from server implementations. This kind of cross-file, cross-service analysis is nearly impossible with chunked retrieval.

Onboarding documentation generation: Load the codebase, existing ADRs, and git history summaries. Ask Scout to generate comprehensive onboarding docs that explain not just what the code does but why key architectural decisions were made.

Compliance checking: Load the full codebase plus GDPR/HIPAA/SOC2 requirement checklists. Ask Scout to produce a compliance report citing specific file locations where requirements are or aren’t met.

When NOT to Use Scout — Matching Context to Your Workload

Scout’s 10M context window is genuinely useful for the cases above, but it’s the wrong choice for many common tasks. Using Scout for small-context tasks wastes money and adds latency.

Don’t use Scout for:

• Single-function code review (use a smaller model with 8K context — 1000× cheaper)
• Simple code generation (Claude Sonnet or GPT-4o-mini are faster and cheaper)
• Real-time autocomplete (latency makes Scout unsuitable for IDE integration)
• Chat interactions where responses need to be under 3 seconds
• Any task where the total prompt is under 100K tokens and you don’t need the model’s coding benchmark performance

Context-right-sizing guide:

In practice: use Scout when you need to analyze something that genuinely requires seeing multiple files together, the combined context exceeds what other models support, and latency isn’t a hard constraint. For everything else, a smaller model is faster, cheaper, and often just as accurate for the narrower task.

FAQ

What is Llama 4 Scout’s actual context window limit?

The model supports 10 million tokens natively, but available context depends on the provider. Groq caps at 2M tokens, Together AI supports up to 4M, and Fireworks offers full 10M on dedicated instances. Local deployment via vLLM can reach 10M with sufficient hardware (3+ H100 80GB GPUs for full context in FP8).

How much does Llama 4 Scout cost per API call?

Groq charges $0.11 per million input tokens and $0.34 per million output tokens. Other providers range from $0.08 to $0.15/M input. Loading a 1M token codebase for analysis costs roughly $0.11–$0.15, far less than comparable large-context proprietary models like Gemini 3 Pro ($12/M input) or Claude Opus 4.5 ($25/M input).

Can Llama 4 Scout run on a consumer GPU?

The Q4_K_M quantized model requires approximately 60GB of VRAM or RAM. This fits on 2× RTX 4090 (48GB, with some context limitations) or a single H100 80GB. Consumer GPUs like the RTX 4090 (24GB) can’t run it in VRAM alone — you’d need CPU offloading via Ollama, which degrades performance significantly. For most developers, the cloud API is more practical than local deployment.

Is Llama 4 Scout better than GPT-4o for code analysis?

On the MBPP coding benchmark, Scout scores 68%, beating GPT-4o. More importantly, for full-repository analysis tasks, Scout’s 10M context window means it literally has access to more code at once, which tends to produce better cross-file analysis regardless of raw benchmark scores. For single-function tasks, GPT-4o is faster and comparably accurate. The right choice depends on the scope of the analysis.

How do I load a large codebase into Scout efficiently?

Flatten the repository into a single text string with file path headers, filter out binary files and generated directories (node_modules, .git, dist), and estimate token count at roughly 4 tokens per line of code. A 500K-line codebase becomes approximately 2M tokens — well within Groq’s 2M limit and comfortably within Together AI’s 4M limit. See the code examples above for a production-ready loader implementation.