> ## 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.

# 2026 LLM Head-to-Head Comparison

> Based on real data from the Model Info overview, this page compares mainstream text LLMs — Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Kimi, Grok, MiniMax — across coding, reasoning, long context, pricing, and context window, helping you quickly pick the right model.

<Note>
  **Data freshness**: As of 2026-07, all model recommendations and benchmark numbers come from the [Model Info overview (APIYI official)](/en/api-capabilities/model-info).
  That document is **continuously updated as vendors ship new models**; for the latest model list and real-time prices, refer to the [APIYI console pricing page](https://www.apiyi.com/account/pricing).
</Note>

<Info>
  **How to read this page**:

  * We focus on **8 task scenarios** (coding, writing, fast response, long context, reasoning, agents, web search, cost control) and give the recommended combos from model-info
</Info>

## 📌 Recommended Combos for 8 Scenarios (Copy-Paste Ready)

> Taken directly from [Model Info overview → Usage Recommendations](/en/api-capabilities/model-info)

### Scenario 1: Coding

| Tier | Recommended Models | Source Notes (model-info verbatim) |
| - | - | - |
| 🏆 **Top performance** | Claude Opus 4.7 · GPT-5.5 · Claude Sonnet 4.6 | "Coding benchmarks +13% vs 4.6 (Opus), SWE-bench 88.7% (GPT-5.5), on par with Opus 4.5 (Sonnet 4.6)" |
| 💰 **Best value** | Gemini 3.5 Flash · GLM-5.1 · Kimi K2.6 · DeepSeek V4 Flash | "Gemini 3.5 Flash fully surpasses 3.1 Pro / GLM-5.1 SWE-Bench Pro 58.4 / Kimi K2.6 overtakes GPT-5.4 and Opus 4.6" |
| 🧰 **Alternatives** | DeepSeek V4 Pro · Qwen3.7-Max · MiniMax M2.7 · o4-mini | — |

### Scenario 2: Writing

| Tier | Recommended Models |
| - | - |
| ⭐ **First choice** | GPT-5.5 · GPT-5.4 · Gemini 3.1 Pro Preview · Claude Opus 4.7 · Claude Sonnet 4.6 |
| 🔁 **Alternatives** | chat-latest · Claude Sonnet 4.5 · GPT-4.1 · GPT-4o · Claude Haiku 4.5 · GLM-4.6 |

### Scenario 3: Fast Response

| Tier | Recommended Models | Source Notes |
| - | - | - |
| ⭐ **First choice** | Gemini 3.5 Flash (\~4x speed) · Claude Haiku 4.5 (2x faster) · GPT-4o Mini | noted in model-info |
| 🔁 **Alternatives** | Gemini 3.1 Flash Lite · Gemini 2.5 Flash · Grok 4 Fast · GPT-4.1 Mini | — |

### Scenario 4: Long Context

| Category | Recommended Models | Context (model-info data) |
| - | - | - |
| 🌍 **Ultra-long context** | Gemini 2.5 Pro · Grok 4 Fast · Grok Code Fast 1 | **2M / 200K / 256K** |
| 💻 **Long context for coding** | GLM-4.6 · Claude 4 series · Kimi K2 | 200K |

<Warning>
  **Long-context caveat**: The numbers above are the **context windows** stated in model-info. The real "effective window" (the longest length at which retrieval stays accurate) is usually below the nominal value; for high-accuracy domains like finance and healthcare, combine with RAG chunking.
</Warning>

### Scenario 5: Complex Reasoning

| Model | Benchmark (stated in model-info) | Notes |
| - | - | - |
| **GPT-5.5 Pro** | Terminal-Bench 2.0 **82.7%** | **`/v1/responses` endpoint + SVIP group only**, expensive |
| **Claude Opus 4.7 Thinking** | Adaptive chain-of-thought, enhanced deep reasoning | 1M (Beta) |
| **GPT-5.5** | SWE-bench Verified 88.7%, new **`xhigh` reasoning tier** | best value pick |
| **o3** | Reasoning model, price cut significantly | 200K context, balances performance and cost |
| **o4-mini** | Lightweight reasoning model | 200K |

<Tip>
  **How to enable reasoning tiers**:

  * GPT-5.5 / GPT-5.5 Pro default to `medium`; to use `xhigh`, pass `reasoning_effort: xhigh` in the request
  * GPT-5.5 Pro only works on `/v1/responses`, not `/v1/chat/completions`
  * Do not use the GPT Pro series for everyday tasks — a single call can cost several dollars
</Tip>

### Scenario 6: Agents

| Model | Key Capability (model-info verbatim) |
| - | - |
| **Kimi K2.5** | Natively multimodal, **Agent Swarm with 100 cooperating agents** |
| **Qwen3.7-Max** | **35-hour autonomous long-horizon agent tasks**, AA Intelligence Index 56.6, global top five |
| **Claude Opus 4.7** | 3x production tasks, tool errors cut to 1/3 |
| **GPT-5.3 Codex** | SWE-Bench Pro SOTA, complex coding and agentic tasks |
| **GPT-5.4** | Native computer use, GDPval 83% |
| **MiniMax M2.7** | Self-evolving, smallest Tier-1 at 10B parameters, open source |

### Scenario 7: Web Search

| Model | Notes |
| - | - |
| **Grok 4 All** · **Grok 3 All** | **Native web access** (no tool calls needed); good for real-time information, news, and market analysis |

### Scenario 8: Cost Control

| Model | Price Data (stated in model-info) |
| - | - |
| **MiniMax M2.7 standard** | **\$0.3 / million input tokens** |
| **MiniMax M2.7 highspeed** | **\$0.6 / million input tokens** (`MiniMax-M2.7-highspeed`) |

<Info>
  **Prices for other models**: model-info does not state explicit input/output prices for every model. All prices are governed by the [APIYI console pricing page](https://www.apiyi.com/account/pricing).
  With "source-forwarded routes + fixed 1:7 exchange rate + recharge bonuses" combined, your effective price is usually below going direct to the vendors — see the console for exact numbers.
</Info>

## 🧮 Coding Benchmark Reference Table

> Only models with **explicit numbers** in model-info are listed. For entries with question marks or no numbers, see the full descriptions in model-info.

| Model | Benchmark | Context | Category |
| - | - | - | - |
| **GPT-5.5 Pro** | Terminal-Bench 2.0 **82.7%** | 1M | Reasoning |
| **GPT-5.5** | SWE-bench Verified **88.7%** | 1M | Coding |
| **GPT-5.5** | Hallucination rate **down 60%** vs 5.4 | 1M | Quality |
| **GPT-5.4** | GDPval **83%** | 1M | Agents |
| **GPT-5.3 Codex** | SWE-Bench Pro **SOTA** | 128K | Coding |
| **GPT-5.2** | GDPval **70.9%** (beats professionals) | 400K | Coding & planning |
| **GPT-5.1** | SWE-bench **76.3%** | 128K | Coding |
| **Claude Opus 4.7** | Coding benchmarks **+13%** vs 4.6 | 1M (Beta) | Coding |
| **Claude Opus 4.7** | **3x** production tasks, tool errors cut to 1/3 | 1M (Beta) | Agents |
| **Kimi K2.6** | SWE-Bench Pro **58.6** (overtakes GPT-5.4 and Opus 4.6) | 256K | Coding |
| **GLM-5.1** | SWE-Bench Pro **58.4** | — | Coding |
| **MiniMax M2.7** | SWE-bench Pro **56.22%** (smallest Tier-1 at 10B params) | standard | Coding |
| **MiniMax M2.5** | SWE-bench **80.2%** | standard | Coding |
| **Qwen3.7-Max** | AA Intelligence Index **56.6** (global top five, #1 in China) | 1M | Agents |

## 📚 Context Window Reference Table

| Model | Context Window | Source |
| - | - | - |
| **Gemini 2.5 Pro** | **2M** | long-context recommendation |
| **GPT-5.5 Pro** · **GPT-5.5** · **GPT-5.4** · **Claude Opus 4.7** · **Claude Opus 4.7 Thinking** · **Qwen3.7-Max** | **1M** | stated in model-info |
| **GPT-5.2** · **chat-latest** | 400K | stated in model-info |
| **Kimi K2.6** | 256K | stated in model-info |
| **GPT-5.1** · **GPT-5.3 Codex** · **GPT-5** · **GPT-5 Mini** · **GPT-5 Nano** · **GPT-4.1** · **GPT-4.1 Mini** · **GPT-4o** · **GPT-4o Mini** · **o3** · **o4-mini** | 128K / 200K | stated in model-info |
| **Grok 4 Fast / Grok Code Fast 1** | 200K / 256K | long-context recommendation |
| **GLM-4.6 / Claude 4 series / Kimi K2** | 200K | long context for coding |
| **Qwen Max / Qwen Plus / Qwen Turbo** | 32K | stated in model-info |

<Note>
  **Reminder**: context window numbers = **vendor-claimed maximums**.
  Real retrieval accuracy ("needle in a haystack") drops significantly at extreme lengths; long context ≠ a replacement for RAG.
</Note>

## 💡 4 Cost-Optimization Tips from model-info (verbatim)

1. **Tier your usage**: cheap models for simple tasks, premium models for complex ones
2. **Test then scale**: prototype with a small model, switch to a large one once requirements are clear
3. **Batch processing**: choose Nano or Mini variants for large volumes of similar tasks
4. **Cache and reuse**: cache results for repeated queries

## ❓ 3 GPT-5 Series Caveats from model-info (verbatim)

<Warning>
  **GPT-5 series usage notes**:

  1. The `temperature` parameter must be set to 1 (only 1 is supported)
  2. Use `max_completion_tokens` instead of `max_tokens`
  3. Do not pass the `top_p` parameter
</Warning>

## 🔗 Related Resources

* [Model Info overview (model-info)](/en/api-capabilities/model-info) — the single source for all data on this page
* [APIYI console pricing page](https://www.apiyi.com/account/pricing) — real-time prices
* [Image & Video Generation Models](/en/api-capabilities/image-video-models) — multimodal model reference
* [API Manual](/en/api-manual) · [Getting Started](/en/getting-started) · [OpenAI-Compatible Calls](/en/api-capabilities/openai/compatible)

<Tip>
  **Still unsure which to pick?** [Contact APIYI support](https://work.weixin.qq.com/kfid/kfc9adfd5810ece25ec) and tell us:

  * Your use case (chat / RAG / agents / writing / ...)
  * Daily call volume
  * Performance requirements (first-token latency, output quality)
  * Budget range

  With these 4 data points we will give you a **tailored model combo**.
</Tip>

<Note>
  **Disclaimer**:
  All data on this page comes from docs/api-capabilities/model-info.mdx (read as of 2026-07).
  model-info is continuously updated as vendors ship models; for the latest recommendations, consult model-info directly or the [APIYI console pricing page](https://www.apiyi.com/account/pricing).
</Note>


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