DeepSeek V3.1 vs Llama 3.3 70B Instruct

Our 12-test suite (scored 1–5) gives DeepSeek V3.1 a clear edge overall, winning 6 benchmarks, losing 3, and tying 3.

Where DeepSeek V3.1 wins:

• Creative Problem Solving: 5 vs 3. DeepSeek V3.1 is tied for 1st among 54 tested models (with 7 others); Llama 3.3 70B Instruct ranks 30th of 54. For tasks requiring non-obvious, feasible ideas, this is a decisive gap.
• Faithfulness: 5 vs 4. DeepSeek V3.1 is tied for 1st among 55 models; Llama 3.3 70B Instruct ranks 34th. When accuracy to source material matters — RAG pipelines, summarization, document Q&A — DeepSeek V3.1 hallucinates less in our testing.
• Persona Consistency: 5 vs 3. DeepSeek V3.1 is tied for 1st among 53 models; Llama 3.3 70B Instruct ranks 45th of 53, near the bottom. For chatbots, roleplay, or character-driven applications, Llama 3.3 70B Instruct is a weak choice.
• Structured Output: 5 vs 4. DeepSeek V3.1 is tied for 1st among 54 models; Llama 3.3 70B Instruct ranks 26th. JSON schema compliance and format adherence are stronger on DeepSeek V3.1, relevant for any API-integrated workflow.
• Strategic Analysis: 4 vs 3. DeepSeek V3.1 ranks 27th of 54; Llama 3.3 70B Instruct ranks 36th. Neither dominates the field here, but DeepSeek V3.1 handles nuanced tradeoff reasoning more reliably.
• Agentic Planning: 4 vs 3. DeepSeek V3.1 ranks 16th of 54; Llama 3.3 70B Instruct ranks 42nd. Goal decomposition and failure recovery are meaningfully better on DeepSeek V3.1, which matters for multi-step autonomous workflows.

Where Llama 3.3 70B Instruct wins:

• Tool Calling: 4 vs 3. Llama 3.3 70B Instruct ranks 18th of 54; DeepSeek V3.1 ranks 47th of 54 — near the bottom of the field. This is the clearest win for Llama 3.3 70B Instruct. For function-calling, argument accuracy, and tool sequencing in agentic pipelines, Llama 3.3 70B Instruct is substantially more reliable.
• Classification: 4 vs 3. Llama 3.3 70B Instruct is tied for 1st among 53 models (with 29 others); DeepSeek V3.1 ranks 31st. High-volume routing and categorization tasks favor Llama 3.3 70B Instruct.
• Safety Calibration: 2 vs 1. Llama 3.3 70B Instruct ranks 12th of 55; DeepSeek V3.1 ranks 32nd. Note: both scores are below the field median of 2, and both are in the lower half of the distribution. Llama 3.3 70B Instruct is less likely to refuse legitimate requests or fail to refuse harmful ones, but neither model excels here.

Ties:

• Long Context: 5 vs 5. Both tied for 1st among 55 models. Retrieval at 30K+ tokens is equivalent.
• Multilingual: 4 vs 4. Both rank 36th of 55. Non-English output quality is equal.
• Constrained Rewriting: 3 vs 3. Both rank 31st of 53. Compression under hard limits is a weak spot for both.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has external scores available: 41.6% on MATH Level 5 (rank 14th of 14 models with that score) and 5.1% on AIME 2025 (rank 23rd of 23). These place it at the bottom of the models tested on those math benchmarks — both well below the field median of 94.15% and 83.9% respectively. DeepSeek V3.1 has no external benchmark scores in the payload, so a direct comparison cannot be made. The Llama 3.3 70B Instruct math scores do confirm it is not suited for advanced quantitative or competition-math tasks.

DeepSeek V3.1 costs $0.15/Mtok input and $0.75/Mtok output. Llama 3.3 70B Instruct costs $0.10/Mtok input and $0.32/Mtok output. The output gap is the one that matters most in practice, since most applications are output-heavy.

At 1M output tokens/month: DeepSeek V3.1 costs $0.75 vs Llama 3.3 70B Instruct's $0.32 — a difference of $0.43. Negligible for any serious project.

At 10M output tokens/month: $7.50 vs $3.20 — a $4.30 gap. Still minor.

At 100M output tokens/month: $75 vs $32 — a $43 gap. Now meaningful for cost-sensitive infrastructure, but still within range of a rounding error compared to compute and engineering costs.

At 1B output tokens/month: $750 vs $320 — a $430/month gap. At this scale, the 2.3x price ratio becomes a genuine procurement decision.

Conclusion: For most developers and teams below 100M output tokens/month, the quality wins DeepSeek V3.1 demonstrates likely justify the premium. High-volume commodity pipelines — batch classification, high-throughput summarization — are where Llama 3.3 70B Instruct's lower price and competitive classification score (tied for 1st in our tests) make it the smarter default.

Choose DeepSeek V3.1 if:

• You need strong faithfulness and low hallucination in RAG or document-grounded tasks (5 vs 4 in our tests)
• Your application involves persona maintenance, chatbots, or character-driven interfaces (5 vs 3)
• You're building agentic workflows with multi-step planning and failure recovery (4 vs 3, ranks 16th vs 42nd of 54)
• JSON schema compliance and structured output reliability matter (5 vs 4, tied for 1st vs 26th)
• You need creative ideation or non-obvious problem solving (5 vs 3, tied for 1st vs 30th)
• Your volume is under 100M output tokens/month, where the $0.43/million premium is negligible

Choose Llama 3.3 70B Instruct if:

• Tool calling and function accuracy are central to your pipeline (4 vs 3, ranks 18th vs 47th of 54 — DeepSeek V3.1 is near the bottom of the field here)
• You're running high-volume classification or routing (tied for 1st vs 31st)
• Cost is the primary constraint at scale — $0.32/Mtok output is 2.3x cheaper than DeepSeek V3.1
• You need a larger context window: Llama 3.3 70B Instruct supports 131,072 tokens vs DeepSeek V3.1's 32,768
• You need higher max output length: 16,384 tokens vs 7,168
• Do not choose Llama 3.3 70B Instruct for math-intensive applications — its 5.1% on AIME 2025 and 41.6% on MATH Level 5 (both last in their respective rankings per Epoch AI) confirm it is not suited for quantitative reasoning