Grok 4.1 Fast vs o3

Across our 12-test internal benchmark suite, the two models split the head-to-head: Grok 4.1 Fast wins 2 tests outright, o3 wins 2, and they tie on 8.

Where Grok 4.1 Fast wins:

• Classification (4 vs 3): Grok 4.1 Fast scores 4/5, ranking tied for 1st of 53 tested models (with 29 others). o3 scores 3/5, ranking 31st of 53. For routing, tagging, and categorization tasks, this is a meaningful edge in our testing.
• Long context (5 vs 4): Grok 4.1 Fast scores 5/5 on retrieval accuracy at 30K+ tokens, tied for 1st of 55 models (with 36 others), and it supports a 2,000,000-token context window. o3 scores 4/5, ranking 38th of 55, with a 200,000-token context window — one-tenth the capacity. For truly long-document workflows, this gap in both score and raw context size is decisive.

Where o3 wins:

• Tool calling (5 vs 4): o3 scores 5/5, tied for 1st of 54 models (with 16 others) in our testing — covering function selection, argument accuracy, and sequencing. Grok 4.1 Fast scores 4/5, ranking 18th of 54. In agentic workflows that chain multiple tool calls, o3's edge here is real and matters.
• Agentic planning (5 vs 4): o3 scores 5/5, tied for 1st of 54 models (with 14 others). Grok 4.1 Fast scores 4/5, ranking 16th of 54. Goal decomposition and failure recovery are where o3 separates itself most clearly.

Where they tie (8 of 12 tests): Both models score identically on structured output (5/5), strategic analysis (5/5), constrained rewriting (4/5), creative problem solving (4/5), faithfulness (5/5), safety calibration (1/5), persona consistency (5/5), and multilingual (5/5). Neither model distinguishes itself on safety calibration — both rank 32nd of 55 in our testing, a below-median result.

External benchmarks (Epoch AI): o3 carries third-party validation that Grok 4.1 Fast lacks in the payload. On MATH Level 5, o3 scores 97.8% — ranked 2nd of 14 models tracked, above the p50 of 94.15%. On AIME 2025, o3 scores 83.9% (12th of 23, at the p50). On SWE-bench Verified (real GitHub issue resolution), o3 scores 62.3% — 9th of 12 models tracked, below the p50 of 70.8%. No external benchmark scores are present in the payload for Grok 4.1 Fast. These external scores reinforce o3's strength in mathematics and offer an honest picture of its coding capabilities: above-median on math, below-median on software engineering by this external measure.

The price gap here is stark. Grok 4.1 Fast runs $0.20/M input and $0.50/M output; o3 runs $2.00/M input and $8.00/M output — that's 10x more expensive on input and 16x more on output. In practice: at 1M output tokens/month, you pay $0.50 for Grok 4.1 Fast versus $8.00 for o3. At 10M output tokens, that's $5 versus $80. At 100M output tokens — realistic for a production customer-support or classification pipeline — you're looking at $50 versus $800 per month. For cost-sensitive applications like classification, long-context retrieval, or high-volume rewriting, the math strongly favors Grok 4.1 Fast, which matches or beats o3 on those specific benchmarks. The case for paying o3's premium narrows to applications where agentic planning and tool calling are the bottleneck — and even there, you should run your own cost-per-task math given the scale of this gap.

Choose Grok 4.1 Fast if:

• You're running high-volume pipelines where output cost matters — the 16x price difference ($0.50 vs $8/M output tokens) compounds fast at scale.
• Your application involves long documents: Grok 4.1 Fast's 2M-token context window (versus o3's 200K) and higher long-context score (5 vs 4) make it the stronger choice.
• Classification or routing is your primary task — Grok 4.1 Fast scores 4/5 versus o3's 3/5 in our testing.
• You need multilingual, structured output, faithfulness, or persona consistency — both models score identically, so pay the lower price.

Choose o3 if:

• Your application depends on chained tool calls or complex agentic workflows — o3 scores 5/5 on both tool calling and agentic planning (tied for 1st in our testing), versus Grok 4.1 Fast's 4/5 on both.
• You're building math-intensive applications: o3's 97.8% on MATH Level 5 (Epoch AI) puts it among the strongest models available by that measure.
• You need a model with established third-party benchmark validation for stakeholder or compliance purposes.
• Your token volumes are low enough that the $7.50/M output cost premium is not a budget constraint.