- Instruction Following
GLM 4.7 Flash is a 30B-class Mixture-of-Experts language model from Z.ai, optimized for speed and efficiency while maintaining strong performance on coding and agentic reasoning tasks.
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DeepSeek V3.2 Exp
- Instruction Following
DeepSeek V3.2 Exp is an experimental iteration of DeepSeek’s large language model series, focused on testing advanced reasoning and generation capabilities before they are incorporated into stable releases.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp is an experimental large language model developed by DeepSeek to explore improvements in language understanding, reasoning, and response quality. It is primarily used for research, prototyping, and evaluating new techniques in dialogue, content generation, and task automation. It can also support developers and researchers in benchmarking, ablation studies, and explorations of novel prompting or fine-tuning strategies. It belongs to the broader DeepSeek model family, extending the capabilities and design ideas of earlier DeepSeek V-series models.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, follows instructions, and maintains context to answer questions or assist with complex tasks.
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Code Reasoning
Understands and generates code snippets, explains programming concepts, and helps debug or refactor code across common languages.
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Text Translation
Translates text between multiple languages while aiming to preserve original meaning, tone, and key domain terminology.
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Document OCR
Extracts machine-readable text from images or scanned documents, supporting downstream search, analysis, and transformation workflows.
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Image Understanding
Interprets images by describing contents, detecting objects, and providing contextual insights about scenes or visual elements.
Use cases
6 Most Valuable Use Cases
- Code Generation Assistance
- Multilingual Text Translation
- Customer Support Chatbots
- Legal Document Analysis
- Contract Drafting Support
- Regulation Change Monitoring
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for DeepSeek V3.2 Exp–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~120 tps | 99.99% | $0.20 | $0.60 | 256K |
| DeepSeek | Global | ~180ms | ~80 tps | 99.9% | ~$0.30 | ~$0.90 | ~128K |
| OpenAI-compatible Gateway | US East | ~220ms | ~70 tps | ~99.9% | ~$0.32 | ~$0.96 | ~128K |
| Custom VPC Host | EU West | ~250ms | ~50 tps | ~99.5% | ~$0.35 | ~$1.05 | ~64K |
Performance benchmarks
Technical Specifications
| Metric | DeepSeek V3.2 Exp | OpenAI GPT-4.5 Turbo | Anthropic Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.40 | $5.00 | $3.00 |
| Output Price ($/1M) | $0.80 | $15.00 | $15.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ~60 tps | ~40 tps | ~35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 7.8M
- Completion tokens generated
- 2.3M
- API requests served
- 99.8%
- Avg uptime
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Dynamically route each request to the optimal model across providers based on latency, price, and quality—without changing your integration or redeploying code.
One API, many models -
Cost-Aware Orchestration
Automatically balance premium and budget models with per-call controls, caps, and policies so you can ship fast while keeping AI spend predictable and optimized.
Control spend by design -
Resilient Fallback Flows
Define cascading provider and model fallbacks so timeouts, quota limits, or regional outages transparently fail over—maintaining uptime without custom retry logic.
Fail soft, not hard -
End-to-End Observability
Trace every request across providers with logs, metrics, and structured payloads to debug prompts, compare models, and tune performance from a single dashboard.
See every token -
Task-Level Abstractions
Call high-level tasks like chat, generate, extract, or rank instead of vendor-specific APIs, so you can swap models without rewriting business logic.
Code to tasks, not vendors -
High-Throughput Batch Jobs
Run large-scale inference workloads as managed batches with concurrency, retries, and progress tracking built in—perfect for backfills, evaluations, and data processing.
Crush your backlogs
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a cost-effective general-purpose model for everyday coding, writing, and chatting.
- You need rapid iteration on prototypes where slightly lower reliability is acceptable for speed.
- You need an assistant for routine code refactoring, small bug fixes, and style improvements.
- Your use case involves generating short-form content like emails, summaries, or marketing blurbs.
- Your use case involves lightweight data cleaning, simple transformations, and basic text classification.
- You need an inexpensive model for A/B testing prompts before moving to premium models.
Avoid if...
- You need guaranteed state-of-the-art reasoning performance comparable to the very strongest frontier models.
- Your workload requires strict enterprise compliance certifications and audited security guarantees from major vendors.
- You need highly reliable execution of complex multi-step tools, agents, or autonomous workflows.
- Your workload requires extremely long-context processing of hundreds of pages with minimal hallucinations.
- You need robust safety, red-teaming, and policy controls proven in large-scale regulated deployments.
- Your workload requires tight integration with a specific commercial ecosystem or managed cloud platform.
FAQ
Frequently Asked Questions
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What is DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp is an experimental DeepSeek large language model focused on fast, low-cost text generation for developer workloads.
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What is DeepSeek V3.2 Exp best suited for?
DeepSeek V3.2 Exp is best suited for general coding assistance, tool-using agents, and high-volume chat or completion workloads where cost efficiency matters.
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What is the context window of DeepSeek V3.2 Exp via LLM.API?
DeepSeek V3.2 Exp supports a 32K token context window when accessed through LLM.API.
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What modalities does DeepSeek V3.2 Exp support?
DeepSeek V3.2 Exp currently supports text-in, text-out interactions only through LLM.API.
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How is DeepSeek V3.2 Exp priced on LLM.API?
DeepSeek V3.2 Exp uses LLM.API’s unified per-token pricing; check your LLM.API dashboard for the latest input and output token rates.
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How fast is DeepSeek V3.2 Exp in terms of latency and throughput?
DeepSeek V3.2 Exp is optimized for low latency and high throughput, making it suitable for real-time applications and parallel request loads.
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How do I call DeepSeek V3.2 Exp through the LLM.API?
Use the LLM.API chat or completion endpoint with the model identifier "deepseek-v3.2-exp" and your standard LLM.API authentication header.
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How does DeepSeek V3.2 Exp compare to similar DeepSeek or OpenAI models?
DeepSeek V3.2 Exp generally trades some reasoning depth for higher speed and lower cost compared with frontier flagship models of similar size.
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Does DeepSeek V3.2 Exp support function calling or tools via LLM.API?
Yes, DeepSeek V3.2 Exp supports structured tool or function calling when you pass a tools schema to the LLM.API chat endpoint.
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What are the main limitations of DeepSeek V3.2 Exp?
DeepSeek V3.2 Exp may hallucinate facts, lacks up-to-the-minute knowledge, and should not be used as the sole source for critical decisions.
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