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README.md
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Performance of Step 3.5 Flash measured across **Reasoning**, **Coding**, and **
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### Detailed Benchmarks
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2. Configure Claude Code.
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```
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"PORT": 3456,
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"Providers": [
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{
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"longContext": "stepfun-api,step-3.5-flash",
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"webSearch": "stepfun-api,step-3.5-flash"
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}
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}
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```
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You can now start Claude Code:
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```bash
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# Start Claude
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ccr code
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# restart ccr if configs are changed
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ccr restart
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```
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#### 7.1.4 Use Step 3.5 Flash on Codex
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1. Install Codex
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1. Use the reference environment setup below and configure `MODEL_NAME` to `Step-3.5-Flash`. [https://github.com/stepfun-ai/StepDeepResearch?tab=readme-ov-file#1-environment-setup](https://github.com/stepfun-ai/StepDeepResearch?tab=readme-ov-file#1-environment-setup)
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## 8.
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1. **Token Efficiency**. Step 3.5 Flash achieves frontier-level agentic intelligence but currently relies on longer generation trajectories than Gemini 3.0 Pro to reach comparable quality.
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2. **Efficient Universal Mastery**. We aim to unify generalist versatility with deep domain expertise. To achieve this efficiently, we are advancing variants of on-policy distillation, allowing the model to internalize expert behaviors with higher sample efficiency.
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Performance of Step 3.5 Flash measured across **Reasoning**, **Coding**, and **Agentic Abilities**. Open-source models (left) are sorted by their total parameter count, while top-tier proprietary models are shown on the right. xbench-DeepSearch scores are sourced from [official publications](https://xbench.org/agi/aisearch) for consistency. The shadowed bars represent the enhanced performance of Step 3.5 Flash using [Parallel Thinking](https://arxiv.org/pdf/2601.05593).
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### Detailed Benchmarks
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2. Configure Claude Code.
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To accommodate diverse workflows in Claude Code, we support both **Anthropic-style** and **OpenAI-style** APIs.
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**Option A: Anthropic API style**:
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> If you intend to use the **OpenRouter** API, refer to the OpenRouter integration guide.
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Step 1: Edit Claude Settings. Update `~/.claude/settings.json`.
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> You only need to modify the fields shown below. Leave the rest of the file unchanged.
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```json
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{
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"env": {
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"ANTHROPIC_API_KEY": "API_KEY_from_StepFun",
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"ANTHROPIC_BASE_URL": "https://api.stepfun.ai/"
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},
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"model": "step-3.5-flash"
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}
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```
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Step 2: Start Claude Code.
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Save the file, and then start Claude Code. Run `/status` to confirm the model and base URL.
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```txt
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❯ /status
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─────────────────────────────────────────────────────────────────────────────────
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Settings: Status Config Usage (←/→ or tab to cycle)
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Version: 2.1.1
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Session name: /rename to add a name
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Session ID: 676dae61-259d-4eef-8c2f-0f1641600553
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cwd: /Users/step-test/
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Auth token: none
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API key: ANTHROPIC_API_KEY
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Anthropic base URL: https://api.stepfun.ai/
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Model: step-3.5-flash
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Setting sources: User settings
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```
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**Option B: OpenAI API style**
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> Note: OpenAI API style here refers to the `chat/completions/` format.
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> We recommend using `claude-code-router`. For details, see [https://github.com/musistudio/claude-code-router](https://github.com/musistudio/claude-code-router).
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After Claude Code is installed, install `claude-code-router` :
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```bash
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# install ccr via npm
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npm install -g @musistudio/claude-code-router
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# validate it is installed
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ccr -v
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```
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Add the following configurations to `~/.claude-code-router/config.json`.
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```json
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{
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"PORT": 3456,
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"Providers": [
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{
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"longContext": "stepfun-api,step-3.5-flash",
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"webSearch": "stepfun-api,step-3.5-flash"
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}
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}
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```
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You can now start Claude Code:
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```bash
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# Start Claude
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ccr code
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# restart ccr if configs are changed
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ccr restart
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```
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#### 7.1.4 Use Step 3.5 Flash on Codex
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1. Install Codex
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1. Use the reference environment setup below and configure `MODEL_NAME` to `Step-3.5-Flash`. [https://github.com/stepfun-ai/StepDeepResearch?tab=readme-ov-file#1-environment-setup](https://github.com/stepfun-ai/StepDeepResearch?tab=readme-ov-file#1-environment-setup)
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## 8. Known Issues and Future Directions
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1. **Token Efficiency**. Step 3.5 Flash achieves frontier-level agentic intelligence but currently relies on longer generation trajectories than Gemini 3.0 Pro to reach comparable quality.
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2. **Efficient Universal Mastery**. We aim to unify generalist versatility with deep domain expertise. To achieve this efficiently, we are advancing variants of on-policy distillation, allowing the model to internalize expert behaviors with higher sample efficiency.
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