Llama 3.3 70B Instruct vs Mistral Small 4

Across our 12-test internal benchmark suite (scored 1–5), Mistral Small 4 wins 6 tests, Llama 3.3 70B Instruct wins 2, and 4 tests are tied.

Where Mistral Small 4 leads:

• Structured output: 5 vs 4. Mistral ties for 1st among 54 models; Llama ranks 26th. For JSON schema compliance and format adherence, Mistral is the clear choice.
• Strategic analysis: 4 vs 3. Mistral ranks 27th of 54; Llama ranks 36th. A full point gap here matters for nuanced tradeoff reasoning and analytical tasks.
• Creative problem solving: 4 vs 3. Mistral ranks 9th of 54; Llama ranks 30th. Non-obvious, feasible ideation favors Mistral significantly.
• Persona consistency: 5 vs 3. Mistral ties for 1st among 53 models; Llama ranks 45th — near the bottom. For chatbots, roleplay, or any system requiring stable character, this is a decisive gap.
• Agentic planning: 4 vs 3. Mistral ranks 16th of 54; Llama ranks 42nd. Goal decomposition and failure recovery are substantially better in Mistral, which is critical for agentic and multi-step workflows.
• Multilingual: 5 vs 4. Mistral ties for 1st among 55 models; Llama ranks 36th. For non-English deployments, Mistral is the stronger choice.

Where Llama 3.3 70B Instruct leads:

• Long context: 5 vs 4. Llama ties for 1st among 55 models; Mistral ranks 38th. At 30K+ token retrieval tasks, Llama outperforms — though note Mistral Small 4 offers a larger context window (262,144 tokens vs 131,072).
• Classification: 4 vs 2. Llama ties for 1st among 53 models; Mistral ranks 51st — near the bottom. This is a stark gap: for routing, categorization, and classification pipelines, Llama 3.3 70B Instruct is the significantly better choice.

Tied tests (same score):

• Tool calling: Both score 4, both rank 18th of 54. No meaningful difference for function calling workflows.
• Faithfulness: Both score 4, both rank 34th of 55. Equivalent on sticking to source material.
• Constrained rewriting: Both score 3, both rank 31st of 53. Neither excels at hard character-limit compression.
• Safety calibration: Both score 2, both rank 12th of 55. Neither model is strong here relative to the field — the median is also 2, so this is a common limitation across the landscape.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has scores available on third-party math benchmarks: 41.6% on MATH Level 5 (rank 14 of 14 tested — last among models with this score) and 5.1% on AIME 2025 (rank 23 of 23 — last among models tested). No external benchmark scores are available in our data for Mistral Small 4. These scores indicate Llama 3.3 70B Instruct is not competitive on advanced math olympiad problems; teams with heavy math reasoning requirements should look elsewhere regardless of which model they choose here.

Llama 3.3 70B Instruct costs $0.10/MTok input and $0.32/MTok output. Mistral Small 4 costs $0.15/MTok input and $0.60/MTok output — 50% more on input and 88% more on output. Output tokens dominate most production costs, so the output gap is what matters most in practice.

At 1M output tokens/month: Llama costs $0.32 vs Mistral's $0.60 — a $0.28 difference, negligible for most teams.

At 10M output tokens/month: $3.20 vs $6.00 — a $2.80/month gap, still modest.

At 100M output tokens/month: $320 vs $600 — a $280/month difference that starts to matter at scale.

Who should care: High-volume API consumers running classification pipelines, document summarization at scale, or batch inference workloads will feel this gap. For most developers and consumer-facing applications under 10M output tokens/month, the $2.80 difference is unlikely to drive a decision. The more relevant question is capability fit — but teams operating at 100M+ tokens/month should factor the ~$3,360/year gap into their build vs. cost analysis. Note that Mistral Small 4 also supports image input (text+image->text modality), which Llama 3.3 70B Instruct does not — that additional capability partly explains the price difference and may be worth it if multimodal features are on your roadmap.

Choose Llama 3.3 70B Instruct if:

• Your primary workload is document classification, routing, or categorization — it scores 4 vs Mistral's 2 and ties for 1st among 53 models.
• You need strong long-context retrieval within a 128K window and want to minimize cost — it scores 5 vs 4 and is 47% cheaper on output.
• You're running high-volume batch inference where the $0.28/MTok output cost difference adds up at 100M+ tokens/month.
• Your workflow requires logprobs, min_p, top_k, logit_bias, or repetition_penalty — these parameters are supported by Llama but absent from Mistral Small 4's parameter list in our data.

Choose Mistral Small 4 if:

• You're building agentic or multi-step AI systems — its agentic planning score of 4 vs 3, ranking 16th vs 42nd, is a meaningful advantage.
• Your application involves roleplay, assistants, or character-driven interactions — persona consistency of 5 vs 3 (1st vs 45th of 53) makes Mistral the clear winner.
• You need reliable JSON/structured output in production — 5 vs 4, tied for 1st among 54 models.
• You're deploying in non-English markets — multilingual score of 5 vs 4, tied for 1st among 55 models.
• You need image input support — Mistral Small 4 supports text+image->text modality; Llama 3.3 70B Instruct is text-only.
• You require a longer context window — 262,144 tokens vs 131,072.
• You need built-in reasoning support — Mistral Small 4 supports the reasoning and include_reasoning parameters, which Llama 3.3 70B Instruct does not.