Llama 3.1 405B Instruct
Meta • Text
Context Window128K
Knowledge CutoffUnknown
Llama 3.1 405B Instruct vs DeepSeek V3
Llama 3.1 405B for Meta ecosystem and broad tooling, DeepSeek V3 for coding benchmarks and cost efficiency.
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DeepSeek V3
DeepSeek • Text
Context Window128K
Knowledge CutoffUnknown
Editorial Verdict
DeepSeek V3 is the higher-capability model; Llama 3.1 405B is the largest open model.
DeepSeek V3 outperforms Llama 3.1 405B on most benchmarks despite being smaller, demonstrating superior training efficiency. Both are open models available for self-hosting. DeepSeek V3 wins on capability. Llama 3.1 405B wins on being the largest open model for teams that specifically need 405B parameter scale.
Llama 3.1 405B Instruct
- Largest open-weight model available — 405B parameters
- Fully open for self-hosting with complete transparency
- Suitable for enterprise self-hosted deployments requiring maximum model size
Best for: Enterprise deployments specifically requiring 405B parameter scale
DeepSeek V3
- Higher benchmark performance despite being smaller — better training efficiency
- Lower compute requirements for inference
- More recent model with updated training and knowledge
Best for: Teams wanting maximum capability in a self-hosted open model
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | Llama 3.1 405B Instruct | DeepSeek V3 | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
Llama 3.1 405B Instruct Total
VariableDeepSeek V3 Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Llama 3.1 405B InstructMMLU Signal (Reasoning)DeepSeek V3
69%
69%
Llama 3.1 405B InstructHumanEval Signal (Coding)DeepSeek V3
69%
69%
Llama 3.1 405B InstructAgentic Tooling SignalDeepSeek V3
69%
69%
Llama 3.1 405B InstructMultimodal SignalDeepSeek V3
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
Llama 3.1 405B Instruct Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
DeepSeek V3 Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
API Implementation
Quick start snippets for each provider style.
Meta (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="meta/llama-3.1-405b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)DeepSeek (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="deepseek/deepseek-v3",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose Llama 3.1 405B Instruct when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose DeepSeek V3 when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Frequently Asked Questions
Practical checks before selecting a production model.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
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