Llama 3.1 405B Instruct
Meta • text->text
Context Window131K
Knowledge CutoffJul 23, 2024
Llama 3.1 405B Instruct vs DeepSeek V3 0324
Llama 3.1 405B for Meta ecosystem and broad tooling, DeepSeek V3 for coding benchmarks and cost efficiency.
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DeepSeek V3 0324
DeepSeek • text->text
Context Window164K
Knowledge CutoffMar 24, 2025
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 0324
- 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 0324 | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $4.00 | $0.29 | 🏆 DeepSeek V3 0324 |
| Output (per 1M tokens) | $4.00 | $1.14 | 🏆 DeepSeek V3 0324 |
| 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
$200DeepSeek V3 0324 Total
$27.25Estimated Monthly Savings: $172.75 with DeepSeek V3 0324
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Llama 3.1 405B InstructMMLU Signal (Reasoning)DeepSeek V3 0324
69%
69%
Llama 3.1 405B InstructHumanEval Signal (Coding)DeepSeek V3 0324
76%
76%
Llama 3.1 405B InstructAgentic Tooling SignalDeepSeek V3 0324
84%
84%
Llama 3.1 405B InstructMultimodal SignalDeepSeek V3 0324
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
Llama 3.1 405B Instruct Strengths
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
DeepSeek V3 0324 Strengths
- Strong tool-calling and structured response support.
- 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/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-chat-v3-0324",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose Llama 3.1 405B Instruct when...
- You rely on function calling, tool chaining, and structured output contracts.
- 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 0324 when...
- You rely on function calling, tool chaining, and structured output contracts.
- 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.
Is Llama 3.1 405B better than DeepSeek V3?
It depends on the workload. Llama 3.1 405B and DeepSeek V3 differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.
What's the price difference between Llama 3.1 405B and DeepSeek V3?
Llama 3.1 405B: $4.00 per 1M input tokens. DeepSeek V3: $0.29 per 1M input tokens. Output token pricing typically runs 2-5x higher.
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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