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DeepSeek: DeepSeek V3 0324
DeepSeek
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DeepSeek V3 0324
•
Released 
March 2025

DeepSeek
DeepSeek V3 0324

Open-weight MoE non-reasoning model with front-end coding, Chinese writing and function-calling upgrades over the original DeepSeek V3.

Modality:
Text
Image
Video
PDF
model ID
deepseek/deepseek-chat-v3-0324
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Output Speed *

N/A
tok/s

Intelligence Index *

9.7
/ 100

Context Window *

163840
tokens

Input price

1.74
Anytoken

Output price

6.84
Anytoken
DeepSeek V3 0324: Open-Weight Non-Reasoning Chat with Stronger Math and Front-End Code DeepSeek V3 0324 is DeepSeek's March 24, 2025 checkpoint of its V3 Mixture-of-Experts chat model, with 671B total and 37B active parameters per token. It sits between the original December 2024 V3 and the hybrid-reasoning V3.1 line. DeepSeek reports gains on MMLU-Pro, GPQA, AIME and LiveCodeBench, plus better front-end code generation, Chinese writing and function calling. Weights are MIT-licensed. It suits teams that want direct, non-thinking responses for coding assistants, tool-calling workflows and Chinese-language content. Access DeepSeek V3 0324 through the AnyAPI.ai API.

Performance

Largest Gains Came on AIME and GPQA, Without a Reasoning Mode

DeepSeek reports that V3 0324 raised AIME from 39.6 to 59.4 and GPQA from 59.1 to 68.4 over the original V3, with MMLU-Pro reaching 81.2 and LiveCodeBench 49.2. These are provider-reported figures, and they come without a separate thinking mode. That makes it a direct-answer model with notably stronger math and science accuracy than its predecessor. In production, it fits latency-sensitive coding and tool-calling paths, while hard multi-step reasoning still belongs on a dedicated reasoning model.

Benchmarks

Independent Data: Index Score of 10, Concise Outputs, Wide Provider Speed Spread

Artificial Analysis scores DeepSeek V3 0324 at 10 on its Intelligence Index, below the median of 12 for comparable open-weight non-reasoning models, but above the original V3 at 8. DeepSeek V3.1 non-reasoning scores 14. The model generated 3.8M tokens across the index versus a 6.8M median, so its outputs are concise. Output speed depended on the host: Azure 89.5 tokens/s, DeepInfra (FP4) 43.9, Novita 37.8, on 10,000-token inputs. Artificial Analysis now marks the model deprecated.

Output Speed

*
N/A
tok/s

Intelligence Index

*
9.7
/ 100

MMLU *

Broad world knowledge and problem-solving
82
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
66
%

HLE *

Adherence to multi-step structured instructions.
5
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
41
%

Technical Specifications

What the model supports

DeepSeek V3 0324 takes text and returns text; it has no image, audio or video input. DeepSeek's V3 documentation lists a 128K-token context window, though some hosts expose 163,840 tokens, so confirm the limit for your route. Maximum output tokens are not stated on the model card and should be checked per host. Function calling, JSON output and FIM completion are supported. There is no reasoning mode, so every output token is answer text rather than hidden thinking.
Verified Specifications — 
DeepSeek V3 0324
*
Input modalities
Text
Image
Video
PDF
output modalities
Text
Context window
163840
 tokens
Maximum output tokens
147456
Reasoning
No
Knowledge cutoff
March 2025
Pricing (standard)
1.74
 AnyTokens in
 / 
6.84
 AnyTokens out

Comparison

DeepSeek V3 0324 or DeepSeek V3.1: Is the Older Checkpoint Still Worth Using?

DeepSeek V3.1 is the direct successor in the V3 line, and both models are 671B-class Mixture-of-Experts models with 128K context. The key difference is mode: V3 0324 is non-thinking only, while V3.1 is a hybrid that supports thinking and non-thinking. Artificial Analysis scores V3.1 non-reasoning at 14 versus 10 for V3 0324. The practical decision is whether to keep a known checkpoint or move to the newer one for stronger tool and agent behavior.

Dimension
DeepSeek V3 0324
DeepSeek V3.1 (Non-reasoning)
Context window *
163840
tokens
32768
tokens
Output speed *
N/A
tok/s
0.00
tok/s
Intelligence Index *
9.7
13.7
Input pricing
1.74
AnyToken
1.5
AnyToken
Output pricing
6.84
AnyToken
5.7
AnyToken
Knowledge cutoff *
March 2025
August 2025

Choose DeepSeek V3 0324 when your prompts, evals or output formats are already validated against this exact checkpoint and you want to avoid behavior drift, or when your host serves it with better latency or cost. Choose DeepSeek V3.1 when you want higher independent intelligence scores, optional thinking in a single model, or stronger tool and agent use. For new builds with no pinning requirement, V3.1 is the more sensible default.

Limitations & Trade-offs

Where DeepSeek V3 0324 falls short

1
Low independent intelligence score. Artificial Analysis rates it 10, below the 12 median for comparable open-weight non-reasoning models, and below DeepSeek V3.1 non-reasoning at 14. Provider-reported AIME and GPQA gains are real relative to the original V3, but the model is not competitive with current reasoning models. Teams building hard-problem assistants should evaluate V3.1, V3.2 or R1-0528 instead.
2
Text-only, with no reasoning mode. The model accepts and returns text only, so screenshots, PDFs-as-images or charts need a separate vision model. It also has no thinking toggle: you cannot trade extra latency for deeper multi-step reasoning. Workloads that mix images with text, or that need adjustable reasoning effort, are better served by another model.
3
Superseded and host-dependent. DeepSeek's own deepseek-chat endpoint moved to V3.1 and later to V3.2, and Artificial Analysis marks V3 0324 deprecated. Access now relies on third-party hosts, and independent output speed ranged from 37.8 to 89.5 tokens/s across them. Latency-sensitive products should benchmark their chosen route, and long-term roadmaps should plan a migration to a newer DeepSeek model.
4
Weak factuality signal and older knowledge. Artificial Analysis reports a negative AA-Omniscience Index for this model, a measure that penalizes confident wrong answers, and its knowledge cutoff is July 2024. Fact-heavy question answering, current-events tasks and unsupervised knowledge lookups need retrieval grounding or a more recent model.

Best-Fit Workloads

Where this model earns its place

01

Front-end and web code generation


DeepSeek lists optimized front-end web development as an explicit upgrade, and LiveCodeBench rose from 39.2 to 49.2 over the original V3 (provider-reported). It fits UI scaffolding, component generation and game front-ends where an immediate answer matters. It lacks a thinking mode, so complex architectural refactors or algorithmic debugging are better sent to a reasoning model.

02

Function-calling assistants


The model card reports increased function-calling accuracy over earlier V3 versions and lists JSON output support. With no hidden reasoning tokens, tool-selection steps return directly, which suits routing and lookup agents with defined schemas. For long multi-step agent loops, V3.1 is reported to be stronger on tool and agent use.

03

Chinese-language writing and report analysis


DeepSeek highlights upgraded Chinese writing proficiency and more detailed outputs for Chinese search-based report analysis. This makes the model a practical choice for Chinese content drafting, translation-adjacent tasks and report generation. Its July 2024 knowledge cutoff means current facts should come from retrieved sources rather than model memory.

04

Pinned open-weight baselines


As a dated MIT-licensed checkpoint with published weights, V3 0324 is a stable reference for regression tests and for comparing against V3.1 or newer models on your own prompts. It also helps teams validate whether a migration changes output format or tool-call behavior. It is less suited to being the long-term default for new products.

Pricing in anytokens via AnyAPI
Input
1.74
₳
Output
6.84
₳
Cache write
—
₳
Cache read
0.66
₳
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PerformanceBenchmarksTechnical SpecificationsComparisonLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access DeepSeek V3 0324 via AnyAPI.ai

Access DeepSeek V3 0324 through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate DeepSeek V3 0324 without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access DeepSeek V3 0324 and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test DeepSeek V3 0324 against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use DeepSeek V3 0324 from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use DeepSeek V3 0324 for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

DeepSeek V3 0324 has a 128K-token context window according to DeepSeek's V3 documentation. Some third-party hosts list 163,840 tokens, so the usable limit depends on the route. The model card does not state a maximum output length, so check output limits for your host before generating very long responses.

Yes. The official model card lists function calling, JSON output and FIM completion. DeepSeek also reports that function-calling accuracy improved over earlier V3 versions, fixing previously identified issues. Exact parameter support, such as strict schema enforcement, can vary by host.

No. DeepSeek V3 0324 is a non-reasoning model with no thinking mode or reasoning controls. DeepSeek reports reasoning-benchmark gains over the original V3, including AIME rising from 39.6 to 59.4, but answers are direct. For explicit chain-of-thought reasoning, consider DeepSeek R1-0528 or a hybrid model such as V3.1.

It uses the same model structure as the original V3 but with updated post-training. DeepSeek reports MMLU-Pro rising from 75.9 to 81.2, GPQA from 59.1 to 68.4, AIME from 39.6 to 59.4 and LiveCodeBench from 39.2 to 49.2, plus better front-end code, Chinese writing and function calling.

DeepSeek V3 0324 is billed per token, and the rate depends on the host serving it. Current AnyAPI.ai rates appear in the pricing component on this page. Artificial Analysis described it as relatively expensive compared with similar-size open-weight non-reasoning models, and newer DeepSeek versions are priced materially lower on output.

* Benchmark data source: Artificial Analysis artificialanalysis.ai