GPT-5.4 Nano vs Grok Code Fast 1

GPT-5.4 Nano wins 8 of 12 benchmarks, Grok Code Fast 1 wins 2, and they tie on 2 (tool calling and faithfulness, both scoring 4/5).

Where GPT-5.4 Nano leads:

• Structured output (5 vs 4): Nano ties for 1st of 54 models; Grok Code Fast 1 sits at rank 26 of 54. For any pipeline that depends on reliable JSON schema compliance, this gap matters.
• Strategic analysis (5 vs 3): Nano ties for 1st of 54; Grok Code Fast 1 ranks 36th of 54. A full two-point gap — significant for business analysis, tradeoff reasoning, or decision-support applications.
• Multilingual (5 vs 4): Nano ties for 1st of 55 models; Grok Code Fast 1 ranks 36th of 55. Grok Code Fast 1 is a text-only model and its multilingual performance trails meaningfully.
• Long context (5 vs 4): Nano ties for 1st of 55; Grok Code Fast 1 ranks 38th of 55. The gap in benchmark score is compounded by context window: Nano supports 400K tokens vs 256K for Grok Code Fast 1 — a concrete architectural advantage for document-heavy tasks.
• Persona consistency (5 vs 4): Nano ties for 1st of 53; Grok Code Fast 1 ranks 38th of 53. Relevant for chatbot and roleplay applications.
• Constrained rewriting (4 vs 3): Nano ranks 6th of 53; Grok Code Fast 1 ranks 31st of 53.
• Creative problem solving (4 vs 3): Nano ranks 9th of 54; Grok Code Fast 1 ranks 30th of 54.
• Safety calibration (3 vs 2): Nano ranks 10th of 55 with only one other model sharing its score; Grok Code Fast 1 ranks 12th of 55 but shares its score with 19 other models. Both are above median (p50 = 2), but Nano is the safer choice for applications with compliance requirements.

Where Grok Code Fast 1 leads:

• Agentic planning (5 vs 4): Grok Code Fast 1 ties for 1st of 54 models (with 14 others); Nano ranks 16th of 54. This is the clearest win for Grok Code Fast 1 — goal decomposition and failure recovery are core to agentic coding agents.
• Classification (4 vs 3): Grok Code Fast 1 ties for 1st of 53 models (with 29 others); Nano ranks 31st of 53. For routing, tagging, or categorization tasks, Grok Code Fast 1 has a meaningful edge.

External benchmark: On AIME 2025 (Epoch AI), GPT-5.4 Nano scores 87.8%, ranking 8th of 23 models tested on that external measure. Grok Code Fast 1 has no AIME 2025 score in our data. This positions Nano as a competitive math reasoning model by third-party standards, though we have no equivalent external data point for Grok Code Fast 1 to compare directly.

In summary, GPT-5.4 Nano is the broader performer. Grok Code Fast 1's advantages are concentrated in agentic planning and classification — both real, but narrow.

Both models are priced identically on input at $0.20 per million tokens. The gap is on output: GPT-5.4 Nano costs $1.25/M tokens vs Grok Code Fast 1's $1.50/M — a difference of $0.25/M. At 1M output tokens/month, that's $0.25 saved; at 10M tokens/month, $2.50; at 100M tokens/month, $250. The cost advantage of GPT-5.4 Nano is real but modest at lower volumes. However, Grok Code Fast 1 uses reasoning tokens (noted in its quirks), which can inflate output token counts significantly in agentic or multi-step tasks — meaning real-world costs for Grok Code Fast 1 in those workflows may run noticeably higher than the sticker price suggests. Developers running high-volume agentic pipelines should budget accordingly. For straightforward inference at scale, GPT-5.4 Nano is the cheaper option.

Choose GPT-5.4 Nano if:

• You need strong structured output for JSON-heavy pipelines (5/5, tied 1st of 54)
• Your tasks span multiple languages (5/5, tied 1st of 55 vs Grok Code Fast 1's 4/5 at rank 36)
• You work with long documents — Nano's 400K context window and 5/5 long-context score both outclass Grok Code Fast 1's 256K / 4/5
• You're doing strategic analysis, business reasoning, or decision support (5 vs 3)
• You want multimodal input support (text + image + file vs text-only)
• Cost matters at high volume — $1.25/M output tokens vs $1.50/M
• Math reasoning is relevant — 87.8% on AIME 2025 (Epoch AI) is a strong external signal

Choose Grok Code Fast 1 if:

• You're building agentic coding workflows and need top-tier planning (5/5, tied 1st of 54) with visible reasoning traces you can inspect and steer
• Your pipeline is classification-heavy and you want a top-ranked router (tied 1st of 53)
• You specifically want reasoning token transparency for debugging multi-step agent behavior
• You accept a $0.25/M output premium and potentially higher costs from reasoning token usage as the tradeoff for that agentic planning edge