Grok 4 vs Llama 3.3 70B Instruct

Across our 12-test benchmark suite (scored 1–5), Grok 4 wins 5 benchmarks outright and ties the remaining 7. Llama 3.3 70B Instruct wins none.

Where Grok 4 leads:

• Strategic analysis: Grok 4 scores 5/5 (tied for 1st among 54 models) vs Llama 3.3 70B Instruct's 3/5 (rank 36 of 54). This is the widest gap — two full points — and reflects Grok 4's stronger nuanced tradeoff reasoning with real numbers. For business analysis, competitive research, or financial modeling tasks, this difference is material.
• Faithfulness: Grok 4 scores 5/5 (tied for 1st among 55 models) vs Llama 3.3 70B Instruct's 4/5 (rank 34 of 55). Grok 4 sticks more reliably to source material without hallucinating — important for RAG pipelines and summarization tasks.
• Persona consistency: Grok 4 scores 5/5 (tied for 1st among 53 models) vs Llama 3.3 70B Instruct's 3/5 (rank 45 of 53). A two-point gap on maintaining character and resisting prompt injection — Llama 3.3 70B Instruct ranks near the bottom of tested models here.
• Multilingual: Grok 4 scores 5/5 (tied for 1st among 55 models) vs Llama 3.3 70B Instruct's 4/5 (rank 36 of 55). Grok 4 delivers equivalent quality in non-English languages at the top tier; Llama 3.3 70B Instruct performs adequately but not at the same level.
• Constrained rewriting: Grok 4 scores 4/5 (rank 6 of 53) vs Llama 3.3 70B Instruct's 3/5 (rank 31 of 53). Compression within hard character limits — Grok 4 is meaningfully better for headline writing, tweet-length rewrites, and similar tasks.

Where they tie:

• Classification (4/4, both tied for 1st among 53 models), long context (5/5, both tied for 1st among 55 models), tool calling (4/4, both rank 18 of 54), structured output (4/4, both rank 26 of 54), creative problem solving (3/3, both rank 30 of 54), agentic planning (3/3, both rank 42 of 54), and safety calibration (2/2, both rank 12 of 55) are all dead heats. On these dimensions, paying 46.9x more for Grok 4 buys nothing.

Third-party benchmarks (Epoch AI):

Llama 3.3 70B Instruct has external benchmark scores in the payload: 41.6% on MATH Level 5 and 5.1% on AIME 2025. Both place it last among models we have scores for on those tests (rank 14 of 14 and rank 23 of 23, respectively). This signals Llama 3.3 70B Instruct is not well-suited for advanced mathematical reasoning or olympiad-level problem solving. Grok 4 has no external benchmark scores in our current data, so a direct comparison on these axes isn't possible from our data alone.

Notable context: Both models score 3/5 on agentic planning (rank 42 of 54), which is below the field median of 4. Neither is a top pick for complex multi-step autonomous workflows based on our testing.

The pricing gap between these two models is extreme. Grok 4 costs $3.00/M input tokens and $15.00/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — roughly 30x cheaper on input and 46.9x cheaper on output.

At real-world volumes, this compounds fast:

• 1M output tokens/month: Grok 4 costs $15.00; Llama 3.3 70B Instruct costs $0.32. Difference: $14.68.
• 10M output tokens/month: Grok 4 costs $150.00; Llama 3.3 70B Instruct costs $3.20. Difference: $146.80.
• 100M output tokens/month: Grok 4 costs $1,500.00; Llama 3.3 70B Instruct costs $32.00. Difference: $1,468.00.

For consumer-facing products with high request volumes — chatbots, content pipelines, classification services — the cost difference will dominate the decision. Llama 3.3 70B Instruct is competitive on classification (tied 4/5), long context (tied 5/5), structured output (tied 4/5), and tool calling (tied 4/5), meaning many production workloads can run on it without meaningful quality loss. The cases where Grok 4's extra cost is justified are narrow but real: multilingual deployments, tasks demanding strict source faithfulness, persona-driven applications, and strategic analysis workflows.

Choose Grok 4 if:

• Your workload depends on strategic analysis, faithfulness to source material, or multilingual output — these are the areas where it demonstrably outperforms Llama 3.3 70B Instruct in our testing.
• You're building persona-driven applications (agents, branded chatbots, roleplay) where consistency matters — Grok 4 scores 5/5 vs Llama 3.3 70B Instruct's 3/5.
• You need the 256K context window; Llama 3.3 70B Instruct caps at 131K.
• You require image and file inputs alongside text — Grok 4 supports multimodal input; Llama 3.3 70B Instruct is text-only per our data.
• You need reasoning token support (include_reasoning parameter) for chain-of-thought transparency.
• Volume is low enough that the $15/M output cost is manageable.

Choose Llama 3.3 70B Instruct if:

• You're running classification, long-context retrieval, tool calling, or structured output tasks — it matches Grok 4 exactly on all four in our testing, at 46.9x lower output cost.
• Your budget is the primary constraint. At $0.32/M output tokens, you can run roughly 47 requests for every 1 Grok 4 request at equivalent cost.
• You need parameters like frequency_penalty, presence_penalty, repetition_penalty, min_p, top_k, or stop sequences — these are available on Llama 3.3 70B Instruct but not listed for Grok 4.
• Advanced math reasoning is not a requirement — Llama 3.3 70B Instruct scores 5.1% on AIME 2025 (Epoch AI), so steer clear of olympiad-level math tasks with either model if possible.