# Grok 3 vs GPT-4o: cost and performance benchmark for FinOps teams

> Grok 3 vs GPT-4o on cost per million tokens, latency, and quality scores — with routing recommendations for engineering and FinOps leaders.

- Source: https://spendtensor.com/blog/grok-3-vs-gpt-4o-cost-performance
- Author: Spend Tensor
- Published: Jun 29, 2026
- Category: Research
- Reading time: 10 min
- Topics: Grok 3, GPT-4o, Benchmark, Cost

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Grok 3 entered the frontier-tier conversation with aggressive pricing and a long-context story, while GPT-4o remains the default choice for production workloads at most enterprises. We ran both through the SpendTensor benchmark harness across 12 workload archetypes — classification, retrieval-augmented chat, structured extraction, code generation, agentic tool-use, and long-document summarization — to settle the cost-vs-quality question for FinOps and engineering leaders.

Pricing math: Grok 3 lists at $3.00 / $15.00 per million input/output tokens with prompt caching at a 75% read discount. GPT-4o lists at $2.50 / $10.00 with automatic caching at a 50% discount. On paper GPT-4o wins on raw output cost, but Grok 3's deeper cache discount flips the math for any workload with a stable system prompt reused 6+ times within the 5-minute cache window.

Quality scores (averaged across our 12-task suite, normalized 0–100): GPT-4o lands at 86.4, Grok 3 at 84.1. The gap closes to <1 point on classification, extraction, and summarization — the workloads that dominate enterprise spend — and widens to 5–7 points on multi-step agentic reasoning, where GPT-4o still leads.

Latency: Grok 3 p50 was 280ms vs GPT-4o at 410ms on identical 4k-token prompts. For latency-sensitive surfaces (chat UIs, autocomplete, voice), Grok 3 is meaningfully faster end-to-end despite comparable token throughput.

Cost-per-quality-point (the metric that actually matters): on classification and extraction workloads with stable prompts, Grok 3 delivers 38% lower effective cost than GPT-4o. On agentic and code workloads, GPT-4o is 12–18% cheaper per quality-adjusted token because Grok 3 needs more retries to hit the same eval score.

Recommended routing strategy: send classification, RAG chat, and structured extraction to Grok 3 with explicit cache breakpoints; keep agentic tool-use and complex code generation on GPT-4o; benchmark long-document summarization on your own data because the winner flips based on document structure. The two-model split typically cuts blended spend 22–34% vs single-provider deployments — and SpendTensor's router applies the policy automatically once you tag your workloads.

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Published by SpendTensor — AI spend management and LLM FinOps. Quote with attribution and a link to https://spendtensor.com/blog/grok-3-vs-gpt-4o-cost-performance.