What other models does DeepSeek offer?

DeepSeek builds a family of open-weight models across non-reasoning and reasoning tiers, with each generation expanding context length, intelligence, and cost-efficiency relative to its predecessor. Here are some other models DeepSeek supports:

• DeepSeek V3: The original December 2024 non-reasoning release that launched DeepSeek's open-weight flagship line. It shares the same 128k-token context window as V3.2 but uses a slightly smaller 671B-parameter MoE base, scores lower on the Artificial Analysis Intelligence Index, and costs less at $0.40 input / $0.89 output per 1M tokens

• DeepSeek V4 Flash: A reasoning model released April 2026 with a 1M-token context window, 284B total parameters, and 13B active parameters. It ranks #11 of 89 comparable reasoning models on the Intelligence Index (as of 06/08/2026), runs at 112.9 tokens per second, and is priced at $0.14 input / $0.28 output per 1M tokens, making it the most affordable reasoning option DeepSeek offers

• DeepSeek V4 Pro: DeepSeek's flagship reasoning model 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) and costs $0.435 input / $0.87 output per 1M tokens

• DeepSeek R1: The earlier reasoning-focused model in DeepSeek's lineup, featuring a 128k context window and extended chain-of-thought traces. At $1.35 input / $4.20 output per 1M tokens, it's significantly more expensive than the newer V4 reasoning generation for comparable intelligence levels

How does DeepSeek V3.2 differ from DeepSeek's other models?

DeepSeek V3.2 is the current top-tier non-reasoning model in DeepSeek's V3 series, released in December 2025 as the most capable text-generation model in that family before the V4 reasoning generation.

• Intelligence ranking: DeepSeek V3.2 scores #12 of 43 comparable open-weight non-reasoning models on the Artificial Analysis Intelligence Index (as of 06/08/2026), placing it above average for its class. The earlier DeepSeek V3 ranks #16 on the same leaderboard, a clear step down in measured capability

• Pricing: At $0.50 per 1M input tokens and $1.60 per 1M output tokens, V3.2 costs more than the original DeepSeek V3 ($0.40 input / $0.89 output). The V4 Flash reasoning model undercuts both on blended cost at roughly $0.06 per 1M tokens at typical cache ratios (as of 06/08/2026), though it is a reasoning model with different latency characteristics

• Architecture: DeepSeek V3.2 uses 685B total parameters with 37B active in a Mixture of Experts configuration, a modest increase from DeepSeek V3's 671B total. Both share a 128k-token context window, compared to the 1M-token windows on V4 Flash and V4 Pro

• Reasoning capability: V3.2 is a non-reasoning model that returns direct responses. For tasks that require multi-step logic or traceable chain-of-thought outputs, DeepSeek V4 Flash and V4 Pro are the appropriate options within the same provider family

• Modalities: V3.2 supports text input and text output only, the same as every other model in the V3 series. Multimodal inputs are not available in this line

DeepSeek V3.2 is the best option within the V3 family for non-reasoning production workloads where cost matters more than the extended 1M context or explicit reasoning traces offered by the V4 generation.

What models should I consider using alongside DeepSeek V3.2?

No single model is optimal for every task. Here are models worth pairing with DeepSeek V3.2 depending on what your product needs:

• DeepSeek V4 Pro (DeepSeek): When queries require multi-step reasoning, advanced scientific or mathematical analysis, or complex code generation, route those requests to DeepSeek V4 Pro. Its #3 Intelligence Index ranking (as of 06/08/2026) and 1M-token context window make it the strongest escalation path within the same provider

• Claude Sonnet 4.6 (Anthropic): For structured output generation, nuanced instruction-following, or tasks where predictable formatting under varied prompts is a priority, Claude Sonnet 4.6 provides reliable performance from a provider with strong enterprise compliance tooling

• Gemini 2.5 Flash (Google): For latency-sensitive or high-throughput inference workloads, particularly those involving image or multimodal inputs that DeepSeek V3.2 cannot handle, Gemini 2.5 Flash offers fast output at competitive pricing

• Llama 3.3 70B (Meta): For teams mixing cloud API calls with self-hosted inference, Llama 3.3 70B is a capable open-weight general-purpose model that can absorb a share of V3.2's non-reasoning workload at near-zero marginal cost on owned hardware

• GPT-4o mini (OpenAI): For high-volume, low-complexity tasks like classification, extraction, or short-form rewriting where V3.2's full capability isn't needed, GPT-4o mini offers a broadly available, well-supported fallback at a lower price point

What are the challenges of using DeepSeek V3.2 in my product?

Like any production LLM, DeepSeek V3.2 comes with tradeoffs worth planning for:

• Context window ceiling: The 128k-token context window is adequate for most tasks but is one-eighth the size of the 1M-token windows on DeepSeek V4 Flash and V4 Pro. Applications with very long documents, large codebases, or extended conversation histories will hit this limit and require chunking or context management logic

• No reasoning traces: DeepSeek V3.2 is a non-reasoning model. For tasks where showing work is important, such as step-by-step math, legal analysis, or debugging chains, the model's direct-response format is a constraint compared to the V4 reasoning generation

• Text-only modality: V3.2 accepts and outputs text only. Any pipeline that needs image, audio, or video understanding requires routing those request types to a different model

• Provider dependency: Concentrating inference on DeepSeek creates fragility if the provider has an outage or deprecates the V3.2 checkpoint. Because V3.2 has no direct equivalent at other providers, a failover strategy requires mapping to a comparable model family in advance

• Cost at scale: At $1.60 per 1M output tokens, costs compound quickly for high-volume or verbose workloads. Without per-project budget tracking, a spike in output-heavy requests can produce unexpected billing

Why should I use Merge Gateway to route LLM requests with DeepSeek V3.2 and every other model?

Using DeepSeek V3.2 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• Cost governance: Set hard or soft project budgets so DeepSeek V3.2 spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers, giving you full visibility before costs escalate

• One API, every provider: Access DeepSeek V3.2 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

• 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 V3.2 via Merge Gateway?

Getting DeepSeek V3.2 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 V3.2, the model string is deepseek/deepseek-v3.2. 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 V3.2 as primary with one fallback.

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