Grok Code Fast 1 vs Llama 3.3 70B Instruct

Across our 12-test benchmark suite, Grok Code Fast 1 wins 2 tests outright, Llama 3.3 70B Instruct wins 1, and 9 are ties.

Where Grok Code Fast 1 wins:

• Agentic planning (5 vs 3): This is the decisive gap. Grok Code Fast 1 ties for 1st among 54 models tested (with 14 others); Llama 3.3 70B Instruct ranks 42nd of 54. For tasks like breaking down a complex coding goal into subtasks, handling tool failures gracefully, or running multi-step pipelines, Grok Code Fast 1 is measurably better.
• Persona consistency (4 vs 3): Grok Code Fast 1 ranks 38th of 53; Llama 3.3 70B Instruct ranks 45th of 53. Neither is a standout here — both fall in the lower half — but Grok Code Fast 1 holds character and resists injection more reliably in our tests.

Where Llama 3.3 70B Instruct wins:

• Long context (5 vs 4): Llama 3.3 70B Instruct ties for 1st among 55 models tested (with 36 others); Grok Code Fast 1 ranks 38th of 55. Notably, Grok Code Fast 1 has a larger context window (256K vs 131K), but retrieval accuracy at 30K+ tokens is stronger for Llama 3.3 70B Instruct in our testing. Raw window size and retrieval quality are different things.

Ties across 9 benchmarks:

• Tool calling (4/4), structured output (4/4), classification (4/4), faithfulness (4/4): Both models perform identically and sit around the midfield of our rankings for each.
• Safety calibration (2/2): Both tie at rank 12 of 55 — neither model is a standout at refusing harmful requests while permitting legitimate ones, and both score below the median for this benchmark (p50 is 2, so they're right at it).
• Strategic analysis (3/3), constrained rewriting (3/3), creative problem solving (3/3), multilingual (4/4): Dead heats across the board. On multilingual, both rank 36th of 55; on strategic analysis, both rank 36th of 54.

External benchmark context: Llama 3.3 70B Instruct has third-party data from Epoch AI available: it scores 41.6% on MATH Level 5 (ranked 14th of 14 models with that score in our set) and 5.1% on AIME 2025 (ranked 23rd of 23). These place it at the bottom of the external math benchmarks for models we track — math-intensive tasks are a clear weakness. Grok Code Fast 1 has no external benchmark scores in our data to report.

Grok Code Fast 1 is priced at $0.20/M input and $1.50/M output tokens. Llama 3.3 70B Instruct runs at $0.10/M input and $0.32/M output — half the input cost and less than a quarter of the output cost. At real-world volumes, that gap compounds fast: at 1M output tokens/month, you're paying $1.50 vs $0.32 — a difference of $1.18. At 10M tokens/month that's $11.80 saved; at 100M tokens/month, $118 saved per month just on output. For high-throughput applications — document processing, summarization pipelines, customer support at scale — Llama 3.3 70B Instruct's pricing makes a material difference. Developers building agentic systems that generate long reasoning traces should factor in Grok Code Fast 1's reasoning token overhead (it uses reasoning tokens by default), which can push effective output costs higher than the base rate suggests. If your workload doesn't specifically require agentic planning or visible reasoning traces, Llama 3.3 70B Instruct is the defensible budget choice.

Choose Grok Code Fast 1 if: You're building agentic coding pipelines, autonomous agents, or any system that requires multi-step planning and failure recovery. Its 5/5 on agentic planning (tied for 1st of 54 models) is the single most important differentiator here, and its support for visible reasoning traces (include_reasoning parameter) gives developers a debugging lever that Llama 3.3 70B Instruct doesn't offer. The 256K context window is also useful if your codebase is large enough to stress a 131K limit. Budget: $0.20/$1.50 per million tokens in/out.

Choose Llama 3.3 70B Instruct if: Your workload is general-purpose — summarization, classification, document Q&A, customer support, or anything where agentic planning isn't required. It matches Grok Code Fast 1 on 9 of 12 benchmarks and wins on long-context retrieval, all at $0.10/$0.32 per million tokens. At 10M+ output tokens/month, the savings justify the tradeoff unless you specifically need that agentic planning advantage. Its open-weight lineage also means you can explore self-hosting options if API costs become a constraint at scale. Avoid it for math-heavy tasks — its 5.1% on AIME 2025 (Epoch AI) signals a hard ceiling on formal reasoning.