> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Claude Opus 4.8 Launches: Stronger Coding, More Honest, Same Price
> Anthropic's flagship Claude Opus 4.8 is here. Agentic coding rises from 64.3% to 69.2%, it's 4x less likely to miss code flaws, and adds dynamic workflows plus effort control. APIYI has launched claude-opus-4-8 at the same price as 4.7 ($5/$25 per 1M tokens).
## Key Takeaways
* **Coding evolves again**: Agentic coding benchmark rises from Opus 4.7's 64.3% to **69.2%**; multidisciplinary reasoning with tools climbs from 54.7% to 57.9%
* **More honest & reliable**: About **4x less likely** to overlook code flaws than its predecessor, more likely to flag uncertainty and less likely to make unsupported claims
* **Same price, no markup**: APIYI has launched `claude-opus-4-8` at \$5 input / \$25 output per million tokens — identical to Opus 4.7
* **Dynamic workflows (research preview)**: Orchestrate hundreds of parallel subagents, handling codebase-scale migrations across hundreds of thousands of lines
* **Faster & cheaper**: Fast mode runs **2.5x faster**, regular usage costs less than before, and it works independently for longer on long tasks
## Background
On May 28, 2026, Anthropic officially released its flagship Claude Opus 4.8 — another major upgrade following Opus 4.5, 4.6 and 4.7. Anthropic describes it as having "sharper judgement, more honesty about its progress, and the ability to work independently for longer than its predecessors."
As before, this is less a redesign than a "stronger, steadier, more honest" upgrade: it leads competitors on agentic coding, multidisciplinary reasoning, computer use, financial analysis and knowledge work, while showing clear gains in honesty and alignment — preferring to surface uncertainty rather than paper over gaps with plausible-but-unsupported answers.
Notably, Anthropic also teased that its most advanced **Mythos-class models** are expected "in the coming weeks" — making Opus 4.8 the culmination of this flagship line ahead of Mythos. APIYI launched `claude-opus-4-8` immediately at the same price as Opus 4.7 — effectively a capability upgrade at no extra cost.
## Deep Dive
### Core Features
Agentic coding rises from 64.3% to 69.2%, leading GPT-5.5 and Gemini 3.1 Pro on several key benchmarks
About four times less likely to overlook code flaws than the prior model — a more reliable last line of review
Flags uncertainty more readily, makes fewer unsupported claims, with lower deception rates than Opus 4.7
Research preview: orchestrate hundreds of parallel subagents for codebase-scale migrations
### Performance Highlights
Opus 4.8's gains concentrate on harder, longer tasks that demand judgement:
| Benchmark | Claude Opus 4.8 | Claude Opus 4.7 | Gain |
| ---------------------------------------- | ---------------- | --------------- | ---------- |
| **Agentic coding** | **69.2%** | 64.3% | **+4.9pp** |
| **Multidisciplinary reasoning w/ tools** | **57.9%** | 54.7% | **+3.2pp** |
| **Likelihood of missing code flaws** | \~**1/4** of 4.7 | baseline | **-75%** |
| **Fast mode speed** | **2.5×** | baseline | **+150%** |
Source: Anthropic's official announcement (May 28, 2026), with independent reporting from TechCrunch, MacRumors and Axios. On some benchmarks (e.g. terminal coding) GPT-5.5 still leads, so evaluate against your own use case when choosing a model.
**Coding & reliability**:
* Stronger agentic coding, leading contemporaneous competitors on several key benchmarks.
* About 4x less likely to overlook code flaws — well suited as the final gate before shipping.
**Honesty & alignment**:
* More likely to surface "I'm not sure" rather than offer plausible-but-baseless answers.
* Lower deception rate than Opus 4.7; alignment assessments show higher prosocial traits.
**Speed & cost**:
* Fast mode runs 2.5x faster, and regular usage costs less than the prior mechanism.
* Works independently for longer than its predecessors, reducing manual intervention.
### Technical Specifications
| Parameter | Specification |
| --------------------- | ---------------------------------------------------------------------- |
| **Model ID** | `claude-opus-4-8` |
| **Context length** | 200,000 tokens |
| **Reasoning control** | effort parameter (low / medium / high / xhigh / max) |
| **API formats** | OpenAI-compatible / Anthropic-native |
| **Availability** | Anthropic API, AWS Bedrock, Google Vertex AI, Microsoft Foundry, APIYI |
### What's New
* **Dynamic Workflows (research preview)**: Let Claude orchestrate hundreds of subagents in parallel, tackling large-scale jobs such as codebase migrations spanning hundreds of thousands of lines.
* **Effort control**: Adjust how much "effort" the model puts into a single response directly in Claude.ai and Cowork, trading off depth vs. speed.
* **Mid-task instructions in Messages API**: System entries can now be inserted within the message array, allowing instructions to be updated mid-task without restarting the session.
## Practical Applications
### Recommended Scenarios
1. **Large repo-scale coding**: cross-file refactors, complex bug fixes, architecture decisions
2. **Codebase-scale migrations**: parallelize hundreds of thousands of lines via dynamic workflows
3. **Critical code review**: 4x lower chance of missing flaws — ideal for gating important PRs
4. **Long-horizon agent tasks**: research/code/browser agents that run longer with sharper judgement
5. **High-trust knowledge work**: financial analysis and domains that need honest uncertainty flagging
### Code Examples
#### OpenAI-Compatible Format
```python theme={null}
from openai import OpenAI
client = OpenAI(
api_key="your-apiyi-key",
base_url="https://api.apiyi.com/v1"
)
response = client.chat.completions.create(
model="claude-opus-4-8",
messages=[
{
"role": "user",
"content": "Review this TypeScript code and point out potential bugs and design improvements."
}
],
extra_body={
"anthropic_effort": "xhigh" # low / medium / high / xhigh / max
}
)
print(response.choices[0].message.content)
```
#### Anthropic-Native Format
```python theme={null}
import anthropic
client = anthropic.Anthropic(
api_key="your-apiyi-key",
base_url="https://api.apiyi.com"
)
message = client.messages.create(
model="claude-opus-4-8",
max_tokens=8192,
messages=[
{
"role": "user",
"content": "Walk through the root cause of this production bug step by step and propose a fix."
}
]
)
print(message.content[0].text)
```
#### Using in Claude Code
```json theme={null}
{
"model": "claude-opus-4-8",
"apiKey": "your-apiyi-key",
"baseURL": "https://api.apiyi.com/v1"
}
```
Use `claude-opus-4-8` for everyday coding; before critical PRs or complex refactors, run `/ultrareview` for deep review to take full advantage of its "4x fewer missed flaws" reliability.
### Best Practices
1. **Pick by task**:
* **Everyday code/refactor/review**: `claude-opus-4-8` + `xhigh` effort
* **Complex root-cause / multi-step planning**: raise effort to `max`
* **Cost-sensitive batch jobs**: use `medium` effort or the Sonnet line
2. **Leverage dynamic workflows**:
* For large migrations and cross-repo bulk changes, let the model orchestrate subagents in parallel.
3. **Lean on honesty**:
* In financial analysis and compliance review, let the model flag uncertainty to lower the cost of misjudgement.
4. **Use the full context**:
* 200K tokens covers full project source + docs for repo-scale operations in one pass.
## Pricing & Availability
### Pricing
| Item | Claude Opus 4.8 | Claude Opus 4.7 | Change |
| ---------- | --------------------- | --------------------- | --------- |
| **Input** | \$5 / million tokens | \$5 / million tokens | unchanged |
| **Output** | \$25 / million tokens | \$25 / million tokens | unchanged |
Opus 4.8 keeps Opus 4.7's pricing while meaningfully improving coding, agentic and honesty performance — a "stronger at no extra cost" upgrade.
**Comparison with major competitors** (for reference):
| Model | Input | Output | Positioning |
| ------------------- | ------- | -------- | -------------------- |
| **Claude Opus 4.8** | **\$5** | **\$25** | Top coding / agentic |
| Claude Sonnet 4.6 | \$3 | \$15 | Everyday value |
| GPT-5.5 | \$1.25 | \$10 | Coding competitor |
| Gemini 3.1 Pro | \$2 | \$12 | General flagship |
### Stack with Recharge Promotions
Combine with APIYI recharge bonus promotions to further lower real costs: `docs.apiyi.com/faq/recharge-promotions`.
### Where to Get It
**APIYI platform**:
* Website: `apiyi.com`
* OpenAI format: `https://api.apiyi.com/v1`
* Anthropic-native format: `https://api.apiyi.com`
* Model name: `claude-opus-4-8`
**Other channels**:
* Anthropic official API
* AWS Bedrock
* Google Cloud Vertex AI
* Microsoft Foundry
## Summary & Recommendations
Claude Opus 4.8 is a "stronger and more honest at no extra cost" upgrade: agentic coding up to 69.2%, 4x less likely to miss flaws, lower deception rates, pricing held at 4.7's level, plus practical new features like dynamic workflows, effort control and mid-task Messages API instructions.
**Core strengths**:
* **Stronger**: agentic coding 69.2%, multidisciplinary reasoning 57.9%
* **Steadier**: only \~1/4 the chance of missing code flaws vs. the prior model
* **More honest**: flags uncertainty, lower deception than 4.7
* **No markup**: priced identically to Opus 4.7 (\$5/\$25)
**Recommendations**:
1. **Critical coding tasks**: choose `claude-opus-4-8` with `xhigh` effort
2. **Codebase-scale migrations**: enable dynamic workflows for parallel processing
3. **High-value PR review**: use `/ultrareview` in Claude Code
4. **Cost-sensitive batch calls**: use `medium` effort or the Sonnet line
APIYI has fully launched `claude-opus-4-8` with both OpenAI and Anthropic-native formats — try this "stronger at no extra cost" upgrade in your coding and agent workflows today.
Sources: Anthropic official announcement (May 28, 2026), TechCrunch, MacRumors, Axios. APIYI pricing follows the platform's live data. Data retrieved: May 29, 2026.