Compare the listed model data for DeepSeek-V3 by DeepSeek and Llama 3.3 70B Instruct by Meta Llama. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
DeepSeek-V3: 128K tokens; Llama 3.3 70B Instruct: 128K tokens. The listed context-window values are equal. DeepSeek-V3 is listed for coding, agentic workflows, long-context tasks; Llama 3.3 70B Instruct is listed for coding, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: DeepSeek-V3 88.5%; Llama 3.3 70B Instruct 86.0%; HumanEval: DeepSeek-V3 89.9%; Llama 3.3 70B Instruct 88.4%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The first listed parameter quantities are 671B total (37B active via MoE) and 70B; the larger quantity is 9.59× the smaller. Total and active MoE parameters are not directly equivalent.
The catalog release dates are December 2024 and December 2024, about 0 months apart at the precision supplied.
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose DeepSeek-V3 if…
Choose DeepSeek-V3 when your task aligns with catalog-listed uses or strengths such as General-purpose coding and software development, Business document analysis, API building at high quality and low cost, Enterprise AI integration at scale.
Choose Llama 3.3 70B Instruct if…
Choose Llama 3.3 70B Instruct when your task aligns with catalog-listed uses or strengths such as Enterprise text processing at 70B quality, Production chatbots for business, Code assistance, Best performance per dollar in the Llama 3.x family at 70B scale.
Feature Comparison — DeepSeek-V3 vs Llama 3.3 70B Instruct
Catalog specifications for DeepSeek-V3 and Llama 3.3 70B Instruct
Specification
DeepSeek-V3
Llama 3.3 70B Instruct
Provider
DeepSeek
Meta Llama
Parameters
671B total (37B active via MoE)
70B
Context window
128K tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
MIT open source. DeepSeek API: $0.27/MTok input, $1.10/MTok output
Llama 3.3 Community License — free. Third-party: $0.59-0.90/MTok
Listed input/output cost per 1M tokens
input $0.27, output $1.1 per 1M tokens
Separate numeric input/output rates not listed
License
MIT
Meta Llama 3.3 Community License
Coding support
Listed
Listed
Reasoning support
Not listed
Not listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: MMLU
88.5%
86.0%
Benchmark: HumanEval
89.9%
88.4%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between DeepSeek-V3 and Llama 3.3 70B Instruct?
DeepSeek-V3: 128K tokens; Llama 3.3 70B Instruct: 128K tokens. The listed context-window values are equal. DeepSeek-V3 is listed for coding, agentic workflows, long-context tasks; Llama 3.3 70B Instruct is listed for coding, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: DeepSeek-V3 88.5%; Llama 3.3 70B Instruct 86.0%; HumanEval: DeepSeek-V3 89.9%; Llama 3.3 70B Instruct 88.4%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for DeepSeek-V3 and Llama 3.3 70B Instruct?
MMLU: DeepSeek-V3 88.5%; Llama 3.3 70B Instruct 86.0%; HumanEval: DeepSeek-V3 89.9%; Llama 3.3 70B Instruct 88.4%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
DeepSeek-V3: 128K tokens; Llama 3.3 70B Instruct: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-V3 and Llama 3.3 70B Instruct?
DeepSeek-V3: input $0.27, output $1.1 per 1M tokens (MIT open source. DeepSeek API: $0.27/MTok input, $1.10/MTok output). Llama 3.3 70B Instruct: Separate numeric input/output rates not listed (Llama 3.3 Community License — free. Third-party: $0.59-0.90/MTok). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for DeepSeek-V3 and Llama 3.3 70B Instruct?
The catalog lists DeepSeek-V3 as December 2024 and Llama 3.3 70B Instruct as December 2024, about 0 months apart at the precision supplied.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between DeepSeek-V3 and Llama 3.3 70B Instruct?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.
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