GPT-5.4 vs Grok 4

Across the 12 internal benchmarks where both models were tested, GPT-5.4 wins 4, Grok 4 wins 1, and they tie on 7. Here's the breakdown:

Where GPT-5.4 wins:

• Agentic planning: GPT-5.4 scores 5/5 (tied for 1st of 54 models with 14 others) vs Grok 4's 3/5 (rank 42 of 54). This is a substantial gap — it means GPT-5.4 is materially better at goal decomposition and failure recovery in multi-step tasks. For autonomous agents or complex workflow automation, this is a meaningful differentiator.
• Safety calibration: GPT-5.4 scores 5/5 (tied for 1st of 55 with only 4 others — a selective group) vs Grok 4's 2/5 (rank 12 of 55). Safety calibration tests the balance between refusing harmful requests and permitting legitimate ones. Grok 4's score of 2 falls below the field median of 2, placing it in the bottom half. For enterprise or consumer-facing deployments, this gap matters.
• Structured output: GPT-5.4 scores 5/5 (tied for 1st of 54 with 24 others) vs Grok 4's 4/5 (rank 26 of 54). JSON schema compliance and format adherence is critical for API integrations. GPT-5.4's edge here reduces parsing failures in production pipelines.
• Creative problem solving: GPT-5.4 scores 4/5 (rank 9 of 54) vs Grok 4's 3/5 (rank 30 of 54). GPT-5.4 produces more non-obvious, specific, and feasible ideas in our testing.

Where Grok 4 wins:

• Classification: Grok 4 scores 4/5 (tied for 1st of 53 with 29 others) vs GPT-5.4's 3/5 (rank 31 of 53). For categorization and routing tasks, Grok 4 has a real edge — though 30 other models share that top score, so it's not a unique differentiator.

Where they tie (7 benchmarks): Both models score identically on strategic analysis (5/5), constrained rewriting (4/5), tool calling (4/5), faithfulness (5/5), long context (5/5), persona consistency (5/5), and multilingual (5/5). These cover a wide swath of real-world use: reasoning through tradeoffs, staying on-brand, function calling, not hallucinating from source material, handling 30K+ token documents, and non-English outputs.

External benchmarks (Epoch AI data): GPT-5.4 has external benchmark scores in the payload: 76.9% on SWE-bench Verified (rank 2 of 12 models in our dataset, sole holder of that score), and 95.3% on AIME 2025 (rank 3 of 23, sole holder). SWE-bench Verified measures real GitHub issue resolution — a 76.9% score places GPT-5.4 above the dataset median of 70.8% and above the 75th percentile of 75.25%. On AIME 2025 math olympiad problems, 95.3% sits well above the dataset median of 83.9%. No external benchmark scores are present in the payload for Grok 4, so no direct comparison can be made on those dimensions.

Both models charge $15.00/MTok on output, but GPT-5.4 undercuts Grok 4 on input: $2.50 vs $3.00 per million tokens — a 20% gap. At typical API usage where output tokens dominate cost, the difference narrows considerably in practice. At 1M input tokens/month, GPT-5.4 saves $0.50 — negligible. At 10M input tokens/month, the savings reach $5.00. At 100M input tokens/month, GPT-5.4 is $500 cheaper on input alone. For input-heavy workloads like document processing, long-context retrieval, or RAG pipelines — where you push large volumes of text in but generate compact outputs — GPT-5.4's input cost advantage compounds meaningfully. For chat or code generation workloads dominated by output tokens, the $15/MTok output parity means cost is effectively the same. Developers optimizing for cost should factor in their actual input-to-output token ratio before assuming the gap is significant.

Choose GPT-5.4 if: you're building agentic systems or multi-step workflows (scores 5 vs 3 on agentic planning in our testing), need reliable structured output for API integrations (5 vs 4), require strong safety calibration for consumer-facing or enterprise applications (5 vs 2), want the larger context window (1,050,000 tokens vs 256,000), or are processing high input-token volumes where the $2.50 vs $3.00/MTok gap adds up. The external benchmark data also supports GPT-5.4 for coding tasks: 76.9% on SWE-bench Verified (Epoch AI) ranks it 2nd of 12 models in our dataset.

Choose Grok 4 if: your primary workload is classification and routing (scores 4 vs 3 in our testing, tied for 1st of 53 models), you specifically need the logprobs and top_logprobs parameters that GPT-5.4 does not support per the payload, or you're working within Grok 4's supported parameter set — particularly if temperature and top_p control over sampling is important to your application. Note that Grok 4 uses reasoning tokens (flagged in our data), which affects how you should budget token costs in reasoning-heavy tasks.