Prompt Chain

Evaluate Claude Opus 4.8 on Advanced Coding Tasks

Prompt chain testing Claude Opus 4.8 on hard coding tasks with adaptive thinking and xhigh effort for complex reasoning and agentic work.

Works with anthropicawsbedrockgooglevertex

92
Spark score
out of 100
Updated 13 days ago
Version 0.121.18
Models
claude 3 opus

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Why it matters

Test and benchmark Claude Opus 4.8's capabilities on complex coding challenges including bug diagnosis across system boundaries, production-quality code generation with error handling, and nuanced code review with prioritized feedback using adaptive thinking at extra-high effort levels.

Outcomes

What it gets done

01

Diagnose bugs across multiple system boundaries using adaptive thinking

02

Generate production-quality code with proper error handling

03

Perform code review with nuanced, prioritized feedback

04

Run evaluations with xhigh effort level and view results

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/pfoo-opus-4-8-coding | bash

Steps

Steps in the chain

01
Set up the example project
02
Set your API key
03
Run the evaluation
04
View results

Overview

Opus 4 8 Coding

Example evaluation suite for testing Claude Opus 4.8 on advanced coding tasks using promptfoo Use when benchmarking Opus 4.8 on complex coding challenges requiring multi-step reasoning, cross-system debugging, or production-grade code generation

What it does

This prompt chain evaluates Claude Opus 4.8 - Anthropic's most capable model for complex reasoning and agentic coding - on hard coding challenges. It tests bug diagnosis across multiple system boundaries, production-quality code generation with proper error handling, and code review with nuanced, prioritized feedback using the xhigh effort level and adaptive thinking.

When to use - and when NOT to

Use this workflow when you need to benchmark or validate Claude Opus 4.8's performance on complex coding tasks that require multi-step reasoning, cross-system debugging, or production-grade code output. It's ideal for evaluating whether Opus 4.8 meets your standards for agentic coding workflows or long-horizon development tasks.

Do NOT use this if you need basic code completion or simple syntax fixes - the xhigh effort level and adaptive thinking overhead are unnecessary for straightforward tasks. Avoid this workflow if you haven't set explicit thinking: { type: adaptive } configuration, as the model will run without extended thinking even at high effort levels.

Inputs and outputs

You provide an Anthropic API key and run the evaluation suite against predefined coding test cases covering bug diagnosis, code generation, and review scenarios.

Integrations

Opus 4.8 is accessible through multiple providers:

  • Anthropic Messages API - Direct access via anthropic:messages:claude-opus-4-8
  • AWS Bedrock - bedrock:us.anthropic.claude-opus-4-8 or bedrock:converse:us.anthropic.claude-opus-4-8
  • Google Vertex AI - vertex:claude-opus-4-8 with config.region: global
  • Azure AI Foundry - Point anthropic:messages:claude-opus-4-8 at https://<resource>.services.ai.azure.com/anthropic via apiBaseUrl

Across all four providers, promptfoo automatically omits unsupported sampling parameters (temperature, top_p, top_k) that Opus 4.8 rejects at the model level.

Getting started

Install and run the evaluation:

npx promptfoo@latest init --example anthropic/opus-4-8-coding
cd opus-4-8-coding

Then execute the test suite:

# Set your API key
export ANTHROPIC_API_KEY=your_api_key_here

# Run the evaluation
npx promptfoo@latest eval

# View results
npx promptfoo@latest view

Technical details

Opus 4.8 builds on 4.7's feature set with no breaking API changes, adding enhanced capability specifically for long-horizon agentic coding and complex reasoning scenarios. Adaptive thinking is opt-in - set thinking: { type: adaptive } to let the model decide when and how much to reason per request. The effort parameter defaults to high; xhigh is available and recommended for coding and agentic work. Pair high effort with a large max_tokens value.

Source README

anthropic/opus-4-8-coding (Claude Opus 4.8 Advanced Coding)

This example exercises Claude Opus 4.8 on hard coding tasks using the xhigh effort level with adaptive thinking.

You can run this example with:

npx promptfoo@latest init --example anthropic/opus-4-8-coding
cd opus-4-8-coding

What This Tests

Claude Opus 4.8 is Anthropic's most capable model for complex reasoning and agentic coding. This example evaluates:

  • Bug diagnosis across multiple system boundaries
  • Production-quality code generation with proper error handling
  • Code review with nuanced, prioritized feedback

Working with Opus 4.8

  • Builds on Opus 4.7. Opus 4.8 supports the same feature set as 4.7 (no breaking API changes) and improves capability on complex reasoning and long-horizon agentic coding.
  • Adaptive thinking is opt-in. Set thinking: { type: adaptive } (as this example does) to let the model decide when and how much to reason per request. Without an explicit thinking block the model runs without extended thinking, even at high effort.
  • effort defaults to high; xhigh is available. Setting effort: high behaves the same as omitting it. Start with xhigh for coding and agentic work, and pair high effort with a large max_tokens.
  • Sampling controls are managed for you. Opus 4.8 rejects temperature, top_p, and top_k at the model level; promptfoo omits them automatically (don't set them in config).

Running the Example

### Set your API key
export ANTHROPIC_API_KEY=your_api_key_here

### Run the evaluation
npx promptfoo@latest eval

### View results
npx promptfoo@latest view

Other providers

Opus 4.8 is also reachable through:

  • AWS Bedrock - bedrock:us.anthropic.claude-opus-4-8 (or bedrock:converse:us.anthropic.claude-opus-4-8)
  • Google Vertex - vertex:claude-opus-4-8 with config.region: global
  • Azure AI Foundry - point anthropic:messages:claude-opus-4-8 at https://<resource>.services.ai.azure.com/anthropic via apiBaseUrl

Across all four providers, promptfoo automatically omits the unsupported sampling parameters (temperature, top_p, top_k) for Opus 4.8. The Anthropic Messages provider also logs a one-time warning if you set them explicitly; the Bedrock, Vertex, and Azure paths omit them silently.

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