What other models does DeepSeek offer?

DeepSeek maintains a growing family of open-weight models spanning non-reasoning, reasoning, and code-specialized tiers, with each generation raising the bar on intelligence, context length, or cost efficiency. Here are some other models DeepSeek supports:

• DeepSeek V3.2: The most capable non-reasoning model in the V3 line, built on a 685B-parameter MoE architecture with 37B active parameters and a 128k-token context window. It scores #12 of 43 comparable models on the Artificial Analysis Intelligence Index (as of 06/08/2026) and is priced at $0.50 per 1M input tokens and $1.60 per 1M output tokens

• DeepSeek V4 Flash: DeepSeek's cost-optimized reasoning model, released April 2026, with a 1M-token context window, 284B total parameters, and 13B active parameters. It scores #11 of 89 comparable reasoning models on the Intelligence Index (as of 06/08/2026), outputs at 112.9 tokens per second, and costs just $0.14 input / $0.28 output per 1M tokens

• DeepSeek V4 Pro: The flagship reasoning model from DeepSeek, also released April 2026, with 1.6 trillion total parameters, 49B active, and a 1M-token context window. It ranks #3 of 89 comparable reasoning models on the Intelligence Index (as of 06/08/2026) at $0.435 input / $0.87 output per 1M tokens

• DeepSeek R1: DeepSeek's original reasoning-focused model, featuring a 128k-token context window and extended chain-of-thought output. It's priced at $1.35 input / $4.20 output per 1M tokens and is best suited for teams already invested in the R-series API surface before the V4 generation launched

How does DeepSeek V3 differ from DeepSeek's other models?

DeepSeek V3 is the original December 2024 release that established DeepSeek's presence in the high-capability open-weight non-reasoning tier, and it's since been superseded by newer V3-series and V4-generation models.

• Intelligence ranking: DeepSeek V3 ranks #16 of 43 comparable non-reasoning open-weight models on the Artificial Analysis Intelligence Index (as of 06/08/2026). The later DeepSeek V3.2 scores #12 on the same leaderboard, making it noticeably more capable for the same use cases

• Pricing: At $0.40 per 1M input tokens and $0.89 per 1M output tokens, DeepSeek V3 is the most affordable model in the V3 family. DeepSeek V3.2 costs $0.50 input and $1.60 output, while the V4 Flash reasoning model undercuts both on blended cost at roughly $0.06 per 1M tokens at a 7:2:1 cache/input/output ratio (as of 06/08/2026)

• Context window: DeepSeek V3 supports 128k tokens, the same as V3.2 but significantly smaller than the 1M-token context available on both V4 Flash and V4 Pro. Teams working with very long documents or large codebases will outgrow V3's window faster

• Architecture generation: DeepSeek V3 uses 671B total parameters with 37B active. V3.2 expanded this slightly to 685B total, and V4 Pro scales to 1.6 trillion total parameters, reflecting a substantially larger base model

• Reasoning capability: DeepSeek V3 is a non-reasoning model. It returns direct responses without chain-of-thought steps. The V4 Flash and V4 Pro models add extended reasoning capability for tasks that benefit from traceable multi-step logic

DeepSeek V3 fits best for teams that need a proven, well-documented open-weight model at minimal cost and don't require a 1M context window or explicit reasoning traces. For most new projects, the newer models in the lineup offer better capability per dollar.

What models should I consider using alongside DeepSeek V3?

No single model is optimal for every task. Here are models worth pairing with DeepSeek V3 depending on what your product needs:

• DeepSeek V4 Pro (DeepSeek): When requests in your pipeline require multi-step logical reasoning, scientific analysis, or complex coding tasks, route them to DeepSeek V4 Pro. Its #3 Intelligence Index ranking (as of 06/08/2026) and 1M-token context window make it the natural escalation path within the same provider family

• Claude Sonnet 4.6 (Anthropic): For instruction-following tasks, structured output generation, or use cases where consistent behavior across diverse prompts is critical, Claude Sonnet 4.6 offers strong reliability from a major western provider with well-tested safety and compliance tooling

• Gemini 2.5 Flash (Google): For high-volume, latency-sensitive inference where you need fast tokens-per-second throughput at a low price, Gemini 2.5 Flash is a strong complement to DeepSeek V3 and covers multimodal inputs that V3 cannot handle

• GPT-4o mini (OpenAI): For lightweight classification, short-form generation, and extraction tasks at scale where the full capability of DeepSeek V3 isn't required, GPT-4o mini offers a widely supported fallback with broad provider availability and predictable pricing

• Llama 3.3 70B (Meta): For teams that want to mix self-hosted inference with API-based calls, Llama 3.3 70B is a capable open-weight general-purpose model that can absorb a portion of DeepSeek V3's workload at lower marginal cost when running on owned hardware

What are the challenges of using DeepSeek V3 in my product?

Like any production LLM, DeepSeek V3 comes with tradeoffs worth planning for:

• Superseded model risk: DeepSeek V3 is no longer the current recommendation in its own family. DeepSeek's documentation and artificialanalysis.ai both note that V3.1 and V3.2 have since launched. Teams building new integrations should plan for version migration as DeepSeek continues updating the V3 line

• Context window ceiling: The 128k-token context window is suitable for many tasks but is one-eighth the size of the 1M-token windows on V4 Flash and V4 Pro. Long-document workflows, extended chat sessions, or large codebase analysis may require chunking logic or a context management layer

• Text-only modality: DeepSeek V3 accepts and outputs text only. There's no support for image, audio, or video inputs. Products that need multimodal understanding will require routing to a different model for those request types

• Provider dependency: Relying on DeepSeek as a single provider introduces fragility if the provider experiences an outage or deprecates the V3 model version. Having a failover strategy to a comparable model from a different provider is essential for production uptime

• Cost at scale: Even at $0.89 per 1M output tokens, costs compound quickly for high-volume, verbose workloads. Without active budget tracking across projects, output token spend can exceed expectations during traffic spikes

Why should I use Merge Gateway to route LLM requests with DeepSeek V3 and every other model?

Using DeepSeek V3 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access DeepSeek V3 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, with no application code changes required

• Intelligent routing and automatic failover: Merge routes around DeepSeek outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40–60% without touching your application code

• Cost governance: Set hard or soft project budgets so DeepSeek V3 spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches DeepSeek. Enforce per-project model and region policies without adding that logic to your application

How can I start routing requests to DeepSeek V3 via Merge Gateway?

Getting DeepSeek V3 running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For DeepSeek V3, the model string is deepseek/deepseek-v3. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming DeepSeek V3 as primary with one fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.