> ## 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.
# Popular Models
> View detailed information on 400+ supported AI models. Recommended popular and practical AI large models for you.
APIYI supports 400+ mainstream AI models. This page provides detailed model information, pricing, and usage instructions.
**Enterprise-grade Professional and Stable AI Large Model API Hub**
All models are officially sourced and forwarded, with \~20% off pricing (combining top-up bonuses and exchange rate advantages), aggregating various excellent large models. No speed limits, no expiration, no account ban risks, pay-as-you-go billing, long-term reliable service.
## 🔥 Currently Recommended Models
The following are currently stably supplied popular models. For the complete model list and real-time pricing, visit the [APIYI Console Pricing Page](https://www.apiyi.com/account/pricing) or the on-site [Model Pricing Overview](/en/models).
**Model Upgrade Recommendations**: We recommend using the latest models for best performance, but please note:
1. **Initial instability is common**: Newly launched models may experience slow responses, timeouts, or occasional errors due to limited compute capacity at the vendor — this typically stabilizes within days to weeks
2. **Check parameter compatibility**: New models may introduce or change parameters (e.g., `max_completion_tokens` replacing `max_tokens`). Before upgrading, verify that your API parameters remain compatible with older models
3. **Always test before going live**: Before deploying a new model to production, thoroughly validate it in a test environment to ensure output quality and API compatibility meet expectations
A "—" in the context column means the vendor has not published a definitive figure; refer to the console and official documentation. All prices are list prices per 1M tokens and can be combined with top-up bonuses.
## Model Categories
### 🤖 OpenAI Series
#### 🆕 Latest Models
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| -------------------- | --------------- | -------------- | ------------------------------------------------------------------------------------------------------------------- | -------------------------------------------- |
| **GPT-5.6 Sol** 🔥 | `gpt-5.6-sol` | 1M | 5.6-gen flagship, Terminal-Bench 2.1 **88.8%**, BrowseComp 90.4%; the `gpt-5.6` alias points here; \$5/\$30 | Frontier intelligence, agent tasks |
| **GPT-5.6 Terra** 🔥 | `gpt-5.6-terra` | 1M | Matches GPT-5.5 performance at **half the price** (\$2.50/\$15), the 5.6-gen workhorse | Everyday development, migration first choice |
| **GPT-5.6 Luna** | `gpt-5.6-luna` | 1M | Lightweight tier at \$1/\$6; weaker beyond 256K context — chunk long documents | High concurrency, cost-sensitive |
| **GPT-5.5 Pro** | `gpt-5.5-pro` | 1M | Deep-reasoning specialist, Terminal-Bench 2.0 82.7%; **`/v1/responses` endpoint + SVIP group only**, very expensive | Top-tier reasoning, research |
| **chat-latest** | `chat-latest` | 400K | Version-less alias, always points to the latest ChatGPT Instant model | Quick writing, conversation |
**GPT-5.6 dual-group supply**: the default official-relay groups (default / svip) match official pricing, with up to 20% top-up bonus (\~83% of list). The `CodexReverse` group carries a default 0.7 discount, better for cost-sensitive batch workloads. Existing GPT-5.5 code only needs the `model` field swapped. See the [GPT-5.6 launch notes](/en/news/gpt-5-6-launch).
**GPT Pro series (e.g. `gpt-5.5-pro`, `gpt-5.4-pro`) usage notes**:
1. **`/v1/responses` endpoint only**: cannot use `/v1/chat/completions` — switch your SDK / code to the Responses API before calling
2. **Very expensive**: a single call may cost several dollars, and it is open to the SVIP group only to prevent accidental use on the Default group
3. **Not recommended for non-professional needs**: use GPT-5.6 Sol / Terra for everyday tasks; Pro suits only research and top-tier work demanding extreme reasoning depth
#### ✅ Stable / Classic
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ----------------- | -------------------- | -------------- | ------------------------------------------------------------------- | -------------------------------------- |
| **GPT-5.5** ⭐ | `gpt-5.5` | 1M | Previous flagship, SWE-bench Verified 88.7%, `xhigh` reasoning tier | Complex agents, professional workflows |
| **GPT-5.4** ⭐ | `gpt-5.4` | 1M | Previous workhorse, native computer use, GDPval 83% | Complex agents, professional workflows |
| **GPT-5.4 Mini** | `gpt-5.4-mini` | 1M | Lightweight, great value | Balance performance and cost |
| **GPT-5.2** | `gpt-5.2` | 400K | GDPval 70.9% surpassing professionals | Programming planning, structured tasks |
| **GPT-5.1** | `gpt-5.1` | 128K | Intelligence-speed balance, SWE-bench 76.3%, 24h cache | General apps, programming |
| **GPT-5.1 Codex** | `gpt-5.1-codex-high` | 128K | Codex coding series (medium / mini tiers also available) | Programming, agent tasks |
| **GPT-5** | `gpt-5` | 128K | Classic flagship stable version | Top-tier reasoning, complex tasks |
| **GPT-5 Mini** | `gpt-5-mini` | 128K | GPT-5 lightweight, excellent performance | Balance performance and cost |
| **o3** | `o3` | 200K | Reasoning model, significantly price-reduced | Complex reasoning, math, programming |
| **o4-mini** | `o4-mini` | 200K | Lightweight reasoning model | Programming |
| **GPT-4.1** | `gpt-4.1` | 128K | Fast, long-standing workhorse | General applications |
| **GPT-4o** | `gpt-4o` | 128K | Balanced multimodal capabilities | General scenarios |
| **GPT-4o Mini** | `gpt-4o-mini` | 128K | Lightweight fast version | Quick response |
**GPT-5 and newer series usage notes**:
1. Temperature parameter `temperature` must be set to 1 (only supports 1)
2. Use `max_completion_tokens` instead of `max_tokens`
3. Do not pass `top_p` parameter
**Image and Video Generation Models** have been moved to a dedicated page. Visit [Image & Video Generation Models](/en/api-capabilities/image-video-models) for the full list and pricing.
### 🎭 Claude Series (Anthropic)
#### 🆕 Latest Models
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ----------------------------- | ------------------------ | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
| **Claude Opus 5** 🔥 | `claude-opus-5` | 1M | Frontier Bench v0.1 **43.3%**, ahead of Fable 5 (33.7%); 128K output, thinking on by default with five effort tiers; \$5/\$25, same as Opus 4.8 | Top-tier coding, complex agents |
| **Claude Opus 5 Thinking** 🔥 | `claude-opus-5-thinking` | 1M | Explicit thinking variant of Opus 5, enhanced deep reasoning | Top-tier reasoning tasks |
| **Claude Sonnet 5** 🔥 | `claude-sonnet-5` | — | SWE-bench Verified **85.2%**, Terminal-Bench 2.1 80.4%, pure AWS official relay with high cache hit rates; \$2/\$10 during the introductory period (\$3/\$15 after Aug 31) | Programming top choice, agent dev |
| **Claude Fable 5** | `claude-fable-5` | — | Mythos-class flagship, near-universal SOTA in software engineering and visual understanding; \$10/\$50 | Top-tier tasks, long async work |
| **Claude Opus 4.8** | `claude-opus-4-8` | 1M (Beta) | Agentic coding 69.2%, missed-defect rate cut to 1/4 of 4.7, dynamic workflows | Top-tier coding, complex agents |
**30-day data retention compliance for Fable 5 / Mythos 5**: because of these models' advanced capabilities, Bedrock and Anthropic run additional checks for high-risk misuse. For these two models, **inputs and outputs are retained for 30 days** to detect serious abuse — accessed by automated safety systems by default, with human review only when a potential harm is flagged. Retention happens on the AWS / Anthropic side; APIYI is a pure transparent proxy and retains nothing itself. **Other Claude models are unaffected.** See the [Fable 5 launch notes](/en/news/claude-fable-5-launch).
#### ✅ Stable / Classic
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ----------------------- | ---------------------------- | -------------- | -------------------------------------------------------------------------------------- | --------------------------------------- |
| **Claude Opus 4.7** ⭐ | `claude-opus-4-7` | 1M (Beta) | Coding benchmarks +13% over 4.6, tool errors cut to 1/3, `-thinking` variant available | Top-tier coding, complex agents |
| **Claude Opus 4.6** | `claude-opus-4-6` | 1M (Beta) | Terminal-Bench 2.0 #1, 128K output | Top-tier coding, complex agents |
| **Claude Sonnet 4.6** ⭐ | `claude-sonnet-4-6` | 1M (Beta) | Rivals Opus 4.5, great value | Programming, agent dev |
| **Claude Opus 4.5** | `claude-opus-4-5-20251101` | 200K | SWE-bench 80.9%, price reduced to 1/3 | Complex programming, top-tier reasoning |
| **Claude Sonnet 4.5** | `claude-sonnet-4-5-20250929` | 200K | World-class coding, SWE-bench 77.2%, `-thinking` variant available | Code generation, agent development |
| **Claude Haiku 4.5** ⭐ | `claude-haiku-4-5-20251001` | 200K | High cost-performance, SWE-bench 73.3%, 2x speed | Real-time chat, pair programming |
| **Claude 4 Sonnet** | `claude-sonnet-4-20250514` | 200K | Long-standing stable version | Code generation, analysis |
**Latest**: Claude Opus 5 approaches Fable 5 intelligence at half the price (\$5/\$25, same as Opus 4.8) — a painless upgrade. Sonnet 5 is billed as the most agentic Sonnet yet, close to Opus 4.8 but faster and cheaper. **Stable**: Opus 4.7 and Sonnet 4.6 are battle-tested for production workflows already in place; Haiku 4.5 offers 2x speed at great value.
### 🌟 Google Gemini Series
#### 🆕 Latest Models
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ---------------------------- | ------------------------ | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------- |
| **Gemini 3.6 Flash** 🔥 | `gemini-3.6-flash` | 1M | Multimodal flagship Flash, 1,048,576 input / 65,536 output, thinking on by default (four tiers); search grounding, code execution, URL context and Computer Use all enabled; \$1.50/\$7.50 | Programming, agents, multimodal |
| **Gemini 3.5 Flash-Lite** 🔥 | `gemini-3.5-flash-lite` | 1M | High-frequency lightweight tier, **zero thinking by default, \~2 second responses**; \$0.30/\$2.50 | High concurrency, low latency, batch |
| **Gemini 3.5 Flash** | `gemini-3.5-flash` | 1M | Terminal-Bench 2.1 76.2%, \~4x faster at \~half the price (\$1.50/\$9.00) | Programming, cost-performance |
| **Gemini 3.1 Pro Preview** | `gemini-3.1-pro-preview` | 1M | ARC-AGI-2 77.1% (2x+ over 3 Pro), Google's strongest reasoning model | Complex reasoning, multimodal analysis |
| **Gemini 3.1 Flash Lite** | `gemini-3.1-flash-lite` | 1M | GA version, 64% faster than 2.5 Flash, \$0.25/\$1.50 | High concurrency, batch, low cost |
**Note**: Gemini 3 Pro Preview was discontinued on March 9, 2026. Please migrate to Gemini 3.1 Pro Preview or Gemini 3.6 Flash.
#### ✅ Stable / Classic
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| -------------------------- | ------------------------ | -------------- | ---------------------------------------------------------- | ---------------------------------- |
| **Gemini 3 Flash Preview** | `gemini-3-flash-preview` | 1M | SWE-bench 78%, thinking / nothinking variants available | Programming, cost-performance |
| **Gemini 2.5 Pro** ⭐ | `gemini-2.5-pro` | 2M | Official release, programming advantage, strong multimodal | Long text, programming, multimodal |
| **Gemini 2.5 Flash** ⭐ | `gemini-2.5-flash` | 1M | Fast speed, low cost, official release | Quick response scenarios |
| **Gemini 2.5 Flash Lite** | `gemini-2.5-flash-lite` | 1M | Ultra-lightweight, faster and cheaper | Large-scale simple tasks |
**Latest**: Gemini 3.6 Flash and 3.5 Flash-Lite were validated end to end with 30+ test cases each on both the native Gemini format and the OpenAI-compatible format, with all native tools enabled. Read the [3.6 Flash overview](/en/api-capabilities/gemini-3-6-flash/overview) and [3.5 Flash-Lite overview](/en/api-capabilities/gemini-3-5-flash-lite/overview) before integrating. **Stable**: Gemini 2.5 Pro (2M context) and Gemini 2.5 Flash suit settled production environments.
### 🚀 xAI Grok Series
#### 🆕 Latest Models
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ------------------------- | ----------------------- | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- |
| **Grok 4.5** 🔥 | `grok-4.5` | 500K | Co-trained with Cursor; AA Intelligence Index **54** (4th overall) and #1 on agentic tool calling, Terminal Bench 2.1 83.3%, output token efficiency \~4.2x Opus 4.8; \$2/\$6, matching official pricing | Programming, agent tasks |
| **Grok 4.3** | `grok-4.3` | 1M | Intelligence Index 53, τ²-Bench 98%, IFBench 81%; \$1.25/\$2.50, doubling past 200K | Complex reasoning, general tasks |
| **Grok 4 Fast Reasoning** | `grok-4-fast-reasoning` | 200K | Reasoning mode, 93%+ cheaper than Grok-4 | Complex reasoning |
| **Grok Code Fast 1** | `grok-code-fast-1` | 256K | SWE-bench 70.8%, high-speed generation | Code generation, agent programming |
#### ✅ Stable / Classic
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ------------------ | ---------------- | -------------- | ---------------------------------------------------------- | ----------------------------- |
| **Grok 4.20 Beta** | `grok-4.20-beta` | Standard | Reasoning / multi-agent / non-reasoning variants available | General tasks |
| **Grok 4** | `grok-4` | Standard | Official version; `grok-4-all` adds native web search | General tasks, real-time info |
| **Grok 3** | `grok-3` | Standard | Official stable version | Daily use |
| **Grok 3 All** | `grok-3-all` | Standard | Native web search enhanced | News, market analysis |
| **Grok 3 Mini** | `grok-3-mini` | Standard | Small model with reasoning | Lightweight tasks |
**Grok 4.5 token efficiency**: on SWE Bench Pro it averages just 15,954 output tokens, about 1/4.2 of Opus 4.8 (max), halving task steps and cutting real spend on the same job. Note that requests above 200K tokens are billed at the vendor's higher long-context rate.
### 🔍 DeepSeek Series
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ------------------------ | ---------------------- | -------------- | -------------------------------------------------------------------------- | ---------------------------------- |
| **DeepSeek V4 Pro** 🔥 | `deepseek-v4-pro` | 1M | 1.6T/49B activated, SWE-Verified 80.6 near Claude/Gemini, Hybrid Attention | Complex reasoning, coding, agents |
| **DeepSeek V4 Flash** 🔥 | `deepseek-v4-flash` | 1M | 284B/13B activated, just \$0.14/M input, open-source SOTA value | High concurrency, batch |
| **DeepSeek V3.2** | `deepseek-v3.2` | 128K | GPT-5 level, tool-use in reasoning | Complex reasoning, coding |
| **DeepSeek V3.1** ⭐ | `deepseek-v3-1-250821` | 128K | Mixed reasoning, Think/Non-Think dual modes | Intelligent reasoning, programming |
| **DeepSeek R1** | `deepseek-r1` | 64K | Reasoning model | Math, reasoning |
| **DeepSeek V3** | `deepseek-v3` | 128K | Strong comprehensive capabilities | General scenarios |
### 🐘 Chinese Model Series
#### Zhipu AI (GLM)
**🆕 Latest**: GLM-5.2 | **✅ Stable / Classic**: GLM-5.1, GLM-5, GLM-4.6
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| -------------- | --------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------- |
| **GLM-5.2** 🔥 | `glm-5.2` | 1M | Direct Alibaba Cloud official relay, AA Intelligence Index **51** (#1 open-source), Terminal-Bench 2.1 81.0, SWE-bench Pro 62.1; \$1.142/\$3.997 | Project-scale coding, long-horizon agents |
| **GLM-5.1** ⭐ | `glm-5.1` | 200K | SWE-Bench Pro 58.4, 744B MoE, MIT open-source | Complex coding, agents |
| **GLM-5** | `glm-5` | 200K | 744B params (40B activated), coding aligned with Claude Opus 4.5 | Complex coding, systems engineering |
| **GLM-4.6** | `glm-4.6` | 200K | Code and reasoning enhanced, stable | Programming, reasoning, agents |
| **GLM-4.5** | `glm-4.5` | 128K | Standard version, strong overall | General scenarios |
**GLM-5.2 Features**:
* Context jumps from GLM-5.1's 200K to 1M tokens — load project-scale code and multiple long documents at once
* 744B MoE, Terminal-Bench 2.1 81.0 / SWE-bench Pro 62.1, leading open-source on long-horizon coding
* Direct official Alibaba Cloud relay with pricing aligned to the vendor (converted at the fixed 1:7 rate); \~85% of list with top-up bonuses
* For long tasks, raise your timeout above 600 seconds. See the [GLM-5.2 launch notes](/en/news/glm-5-2-launch)
#### Alibaba Qwen
**🆕 Latest**: Qwen3.7-Max | **✅ Stable / Classic**: Qwen3.6 series, Qwen Max / Plus / Turbo
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ------------------------ | --------------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------ | ---------------------------------------- |
| **Qwen3.7-Max** 🔥 | `qwen3.7-max` | 1M | AA Intelligence Index 56.6 (global top 5, #1 in China), 35-hour long-horizon agent autonomy; \$1.714/\$5.142 | Agents, multilingual, long text |
| **Qwen3.7-Plus** | `qwen3.7-plus` | 1M | 3.7-gen value tier, \$0.60/\$2.40 | General tasks, batch processing |
| **Qwen3.6-Max-Preview** | `qwen3.6-max-preview` | 1M | #1 on six coding benchmarks (AIME 2025 93%, GPQA 86%) | Programming, reasoning |
| **Qwen3.6-Plus** | `qwen3.6-plus` | 1M | MoE 72B / 18B activated, Terminal-Bench 61.6, chain-of-thought always on | Coding agents |
| **Qwen3.6-Flash** | `qwen3.6-flash` | 1M | 35B-A3B MoE, multimodal, \$0.17/\$1.02 | Cost-effective, multimodal |
| **Qwen3.6 open weights** | `qwen3.6-27b` / `qwen3.6-35b-a3b` | 1M | Open-weight models hosted by APIYI — no GPU rental or inference stack to operate | Self-hosting alternative, cost-sensitive |
| **Qwen Max** | `qwen-max` | 32K | Long-standing stable version | General tasks |
| **Qwen Plus** | `qwen-plus` | 32K | Enhanced version | Cost-effective |
| **Qwen Turbo** | `qwen-turbo` | 32K | Fast version | Low latency |
#### Moonshot Kimi Series
**🆕 Latest**: Kimi K3 | **✅ Stable / Classic**: Kimi K2.6, K2.5, K2
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ---------------------------- | ---------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ |
| **Kimi K3** 🔥 | `kimi-k3` | 1M | 2.8T-parameter open-source flagship, Kimi Delta Attention, **#1 on LMArena Frontend Code Arena**, GPQA Diamond 93.5%, Terminal-Bench 2.1 88.3%; \$3/\$15 flat across the whole context range, cached input as low as \$0.30 | Frontend coding, long-horizon agents |
| **Kimi K2.7 Code** | `kimi-k2.7-code` | 256K | Coding-specialized tier, \$0.95/\$3.99 | Programming |
| **Kimi K2.6** ⭐ | `kimi-k2.6` | 256K | 1T MoE / 32B activated, SWE-Bench Pro 58.6; \$0.60/\$2.40, \~60% of list | Coding, agents |
| **Kimi K2.5** | `kimi-k2.5` | 200K | Native multimodal, Agent Swarm 100 agents | Multimodal, agents |
| **Kimi K2 Official Release** | `kimi-k2-250711` | 200K | Volcano Engine partnership, strong stability | Production environments |
**Kimi K3 usage notes**:
* Exactly 1,048,576 tokens of context with **flat pricing across the whole range** — drop in whole repos or long documents without worrying about tier jumps
* Max output defaults to 131,072 and can be raised to 1,048,576 tokens; thinking runs at full tilt, so budget output tokens accordingly
* Automatic context caching bills cached input at \$0.30/1M (1/10 of the uncached rate), a big win for multi-turn conversations
#### ByteDance Seed Series
**🆕 Latest**: Seed 2.1 Turbo | **✅ Stable / Classic**: Seed 2.0 Pro / Lite / Mini
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| --------------------- | ---------------------------- | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------- |
| **Seed 2.1 Turbo** 🔥 | `dola-seed-2-1-turbo-260628` | 256K | High-frequency production text model; **15/15 test cases passed on both Chat Completions and Responses**, controllable thinking plus implicit/explicit two-layer caching; \$0.50/\$2.50 | High-concurrency production, batch extraction |
| **Seed 2.0 Pro** | `seed-2-0-pro-260328` | — | AIME 2025 98.3, Codeforces 3020, strong agent capability; \$0.50/\$3.00 | Complex reasoning, coding |
| **Seed 2.0 Lite** | `seed-2-0-lite-260428` | — | Cost-effective enterprise tier, image/video/text multimodal input; \$0.25/\$2.00 | Large-scale production |
| **Seed 2.0 Mini** | `seed-2-0-mini-260428` | — | Ultra-light tier, \$0.10/\$0.40 | Large-volume simple tasks |
**Seed 2.1 Turbo enables deep thinking by default**: with no parameters set, even a one-line question emits hundreds of thinking tokens first (a one-sentence self-introduction measured 444 output tokens, 409 of them thinking), and thinking is billed as normal output tokens. For high-frequency short Q\&A, make `thinking: {"type": "disabled"}` your baseline. See the [integration guide](/en/api-capabilities/dola-seed-2-1-turbo/overview).
### 🌐 MiniMax Series
**🆕 Latest**: MiniMax-M3 | **✅ Stable / Classic**: MiniMax-M2.7, M2.5
| Model Name | Model ID | Context Length | Features | Recommended Scenarios |
| ----------------- | -------------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------- |
| **MiniMax-M3** 🔥 | `MiniMax-M3` | 1M | First open-weight flagship combining frontier agentic coding, 1M context and native multimodality; SWE-Bench Pro **59.0** beats GPT-5.5 and Gemini 3.1 Pro; MSA sparse attention cuts long-context inference cost to \~1/20 of the previous generation | Coding, agents, long context |
| **MiniMax-M2.7** | `MiniMax-M2.7` | Standard | 10B params, SWE-bench Pro 56.22%, self-evolving, smallest Tier-1 model | Coding, agents |
| **MiniMax-M2.5** | `MiniMax-M2.5` | Standard | 230B (10B activated), SWE-bench 80.2%, great value | Coding, agents, office automation |
**MiniMax-M3 billing notes**:
* Tiered billing: \$0.30 input / \$1.20 output per 1M tokens for the 0-512K range; anything beyond is billed at a higher tier
* Model names are case-sensitive: `MiniMax-M3` (as are siblings such as `MiniMax-M2.7-highspeed`)
* Estimate costs before running million-token context jobs
## 💰 Pricing Information
### Billing Methods
* **Pay-as-you-go**: Charged based on actual Token usage
* **No minimum charge**: Use what you pay for, balance never expires
* **Real-time deduction**: Fees deducted from balance immediately after each call
### Pricing Advantages
* Official source forwarding with slight price advantages
* Bulk users can contact customer service for better pricing
* New users get 3 million tokens testing credit upon registration
### View Real-time Pricing
Visit the [APIYI Console Pricing Page](https://www.apiyi.com/account/pricing) for the latest pricing on all models, or the on-site [Model Pricing Overview](/en/models).
## 🛠️ Usage Recommendations
### Model Selection Guide
**Programming Development**
* Top performance: Claude Opus 5 (near Fable 5 intelligence at half the price), GPT-5.6 Sol (Terminal-Bench 2.1 88.8%), Claude Sonnet 5 (SWE-bench 85.2%), Kimi K3
* High cost-performance: GPT-5.6 Terra (GPT-5.5-level performance at half price), Gemini 3.6 Flash, GLM-5.2, MiniMax-M3, DeepSeek V4 Flash
* Alternatives: Grok 4.5, Qwen3.7-Max, Kimi K2.6, Gemini 3.5 Flash
**Text Creation**
* Top choice: GPT-5.6 Sol / Terra, Claude Opus 5, Claude Sonnet 5, Gemini 3.6 Flash
* Alternatives: chat-latest, Claude Sonnet 4.6, GLM-5.2, GPT-4.1
**Quick Response**
* Top choice: Gemini 3.5 Flash-Lite (zero thinking by default, \~2s), Claude Haiku 4.5 (2x faster), GPT-5.6 Luna
* Alternatives: Gemini 3.1 Flash Lite, Gemini 2.5 Flash, Seed 2.1 Turbo (disable thinking explicitly)
**Long Text Processing**
* Ultra-long context: Gemini 2.5 Pro (2M), Kimi K3 (1M, flat pricing), MiniMax-M3 (1M), GLM-5.2 (1M), Claude Opus 5 (1M)
* Note: some models switch to a higher billing tier past a threshold (Grok 4.5 above 200K, MiniMax-M3 above 512K) — estimate costs before long jobs
**Image Generation**
* Latest recommendation: gpt-image-2 (native 4K, precise size/quality control), Nano Banana Pro (4K HD, best text rendering)
* High cost-performance: Nano Banana 2 (\$0.055/image, from \$0.025 pay-as-you-go), Nano Banana Lite (\~4s per image, \$0.025/image)
* Professional design: Seedream 5.0 Pro (\$0.12/call, interactive editing + up to 10 reference images), Seedream 5.0 Lite (\$0.035/image)
* Cheapest: gpt-image-2-all (reverse channel, \$0.03/image)
**Video Generation**
* Official relay first choice: VEO 3.1 Official (\$0.3 / \$1.2 per call, 4/6/8s, native synced audio)
* Chinese-vendor workhorses: Seedance 2.0 series (standard / fast / mini), Wan2.7, HappyHorse 1.1
* Note: the Sora 2 channels have been retired — use the options above. See [Image & Video Generation Models](/en/api-capabilities/image-video-models)
**Web Search**
* Native web access: Grok 4 All, Grok 3 All (no tool call needed)
* Search grounding: Gemini 3.6 Flash / 3.5 Flash-Lite (native tools enabled)
### Cost Optimization Recommendations
1. **Tiered Usage**: Use cheaper models for simple tasks, advanced models for complex tasks
2. **Test Optimization**: Test with small models first, use large models after determining needs
3. **Batch Processing**: Choose Luna / Lite / Mini tiers for large volumes of similar tasks
4. **Cache Reuse**: Lean on each vendor's context cache (Kimi K3, Seed 2.1 Turbo and the Claude series bill cached input as low as 1/10)
5. **Turn off unneeded thinking**: some models think by default (Seed 2.1 Turbo, Gemini 3.6 Flash) — disabling it for short high-frequency Q\&A saves both money and time
## 🔗 Related Resources
* [Model Pricing Overview](/en/models) - Full model list with live pricing
* [Model Comparison Testing](https://imagen.apiyi.com/) - Image generation effect comparison
* [Real-time Price Query](https://www.apiyi.com/account/pricing) - Latest pricing information
* [API Documentation](/en/api-manual) - Detailed interface specifications
* [Quick Start](/en/getting-started) - Integration guide
Model list is continuously updated. We will promptly add newly released excellent models. For the latest launches, follow the [Changelog](/en/changelog). For specific model needs or bulk requirements, please contact customer service.