Claude Opus 4.7 vs Grok 4.20

Across our 12-test suite, Claude Opus 4.7 and Grok 4.20 tie on 6 benchmarks, with each model winning 3. There is no dominant model here — the outcome depends entirely on which capabilities matter to your workload.

Where Claude Opus 4.7 wins:

Agentic planning is the clearest differentiator. Opus 4.7 scores 5/5, ranking tied for 1st among 55 models in our testing, while Grok 4.20 scores 4/5 — placing it 17th of 55. For multi-step AI workflows involving goal decomposition and failure recovery, this gap is meaningful. If you're building agentic systems that need to handle complex task chains, Opus 4.7 has a measurable edge here.

Creative problem solving follows the same pattern: Opus 4.7 scores 5/5 (tied for 1st among 9 models out of 55), versus Grok 4.20's 4/5 (ranked 10th of 55). This benchmark tests non-obvious, feasible ideation — the kind of lateral thinking that separates good AI assistants from great ones on open-ended tasks.

Safety calibration is where the gap is widest by rank. Opus 4.7 scores 3/5 and ranks 10th of 56 models — well above the field median of 2/5. Grok 4.20 scores 1/5, ranking 33rd of 56. This benchmark measures whether a model correctly refuses harmful requests while still permitting legitimate ones. Deployments with strict content policy requirements should take this difference seriously.

Where Grok 4.20 wins:

Structured output is Grok 4.20's clearest advantage: 5/5, tied for 1st among 25 models out of 55 tested. Opus 4.7 scores 4/5, ranking a middling 26th of 55. For developers building applications that rely on JSON schema compliance and reliable format adherence — API integrations, data extraction pipelines, tool-heavy workflows — Grok 4.20 is the stronger choice.

Classification follows the same story: Grok 4.20 scores 4/5, tied for 1st among 30 models out of 54, while Opus 4.7 scores 3/5 and ranks 31st of 54. For routing, tagging, or categorization tasks, Grok 4.20 is measurably more accurate in our tests.

Multilingual is Grok 4.20's third win: 5/5, tied for 1st among 35 models out of 56. Opus 4.7 scores 4/5, ranking 36th of 56. For non-English applications — customer support in multiple languages, localization pipelines, multilingual content generation — Grok 4.20 is the better-tested option.

Where they tie:

Six benchmarks end in a dead heat: tool calling (both 5/5, tied for 1st), faithfulness (both 5/5, tied for 1st), strategic analysis (both 5/5, tied for 1st), long context (both 5/5, tied for 1st), persona consistency (both 5/5, tied for 1st), and constrained rewriting (both 4/5, both ranked 6th of 55). On the capabilities most commonly cited as flagship differentiators — reasoning under real constraints, long document understanding, function calling — neither model has a detectable advantage.

Grok 4.20 also supports a broader set of documented parameters, including reasoning traces, logprobs, seed control, and structured outputs, and accepts file inputs alongside text and images. Opus 4.7 accepts text and image inputs. These are practical differences for developers building around specific API features.

The cost gap here is substantial and tilts most decisions toward Grok 4.20 before you even look at benchmarks. Claude Opus 4.7 runs $5 per million input tokens and $25 per million output tokens. Grok 4.20 costs $2 per million input tokens and $6 per million output tokens — a 4.2x difference on output, which is where the real spend accumulates in production.

At 1 million output tokens per month, Opus 4.7 costs $25 versus Grok 4.20's $6 — a $19 gap that's easy to absorb. Scale to 10 million output tokens and you're paying $250 versus $60, a $190 monthly difference that starts to matter. At 100 million output tokens — realistic for any production chatbot or document processing pipeline — the gap widens to $2,500 versus $600, a $1,900 monthly difference that justifies serious architectural evaluation.

Developers running high-throughput agentic pipelines, batch document processing, or customer-facing products at scale should weigh that output cost difference carefully against the specific benchmark advantages Opus 4.7 holds. Consumer users choosing between subscriptions will feel the gap less acutely, but the performance differences are narrow enough that Grok 4.20's pricing advantage is a meaningful signal in its favor.

Choose Claude Opus 4.7 if:

• Your workflow is agentic — multi-step planning, autonomous task execution, failure recovery (scores 5/5 vs Grok 4.20's 4/5 in our testing)
• Creative problem solving is a core capability — open-ended ideation, lateral thinking, non-obvious solutions (5/5 vs 4/5)
• You operate in regulated or safety-sensitive contexts where content policy calibration matters (ranks 10th of 56 vs Grok 4.20's 33rd)
• Output volume is low enough that the 4x output cost premium is manageable

Choose Grok 4.20 if:

• You're building structured data pipelines — JSON extraction, schema-compliant output, API integrations (5/5 vs Opus 4.7's 4/5, ranked 1st)
• Classification and routing are central to your product — automated categorization, intent detection, content tagging (4/5, ranked 1st vs Opus 4.7's 3/5 at rank 31)
• You need strong multilingual output — non-English generation, localization, global customer support (5/5, ranked 1st vs Opus 4.7's 4/5 at rank 36)
• You're running at significant scale — at 100M output tokens per month, Grok 4.20 saves roughly $1,900 over Opus 4.7
• You need granular API control — logprobs, seed, reasoning traces, and file inputs are explicitly supported
• Cost efficiency is a priority and the benchmarks where Opus 4.7 leads aren't central to your use case