DeepSeek V4 Pro vs Llama 3.2 1B Instruct
DeepSeek V4 Pro (2026) and Llama 3.2 1B Instruct (2024) are frontier reasoning models from DeepSeek and AI at Meta. DeepSeek V4 Pro ships a 1m-token context window, while Llama 3.2 1B Instruct ships a 128k-token context window. On MMLU PRO, DeepSeek V4 Pro leads by 67.5 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 1B Instruct is ~1511% cheaper at $0.03/1M; pay for DeepSeek V4 Pro only for reasoning depth.
Decision scorecard
Local evidence first| Signal | DeepSeek V4 Pro | Llama 3.2 1B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and long-context analysis | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Long context |
| Context window | 1m | 128k |
| Cheapest output | $0.87/1M tokens | $0.20/1M tokens |
| Provider routes | 5 tracked | 7 tracked |
| Shared benchmarks | MMLU PRO leader | 4 rows |
Decision tradeoffs
- DeepSeek V4 Pro holds a shared-benchmark lead on MMLU PRO, ahead by 67.5 points.
- DeepSeek V4 Pro has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek V4 Pro uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags DeepSeek V4 Pro for Coding, RAG, and Agents.
- Llama 3.2 1B Instruct has the lower cheapest tracked output price at $0.20/1M tokens.
- Llama 3.2 1B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V4 Pro
$566
Cheapest tracked route/tier: DeepSeek Platform
Llama 3.2 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
Estimated monthly gap: $494. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Fireworks AI, and Vercel AI Gateway; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.67/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on Fireworks AI, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek V4 Pro is $0.67/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- DeepSeek V4 Pro adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-24 | 2024-09-25 |
| Context window | 1m | 128k |
| Parameters | 1.6T | 1.23B |
| Architecture | Mixture of Experts (MoE) with CSA+HCA hybrid attention | decoder only |
| License | MIT(OSI) | Llama 3 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | DeepSeek V4 Pro | Llama 3.2 1B Instruct |
|---|---|---|
| Input price | $0.43/1M tokens | $0.03/1M tokens |
| Output price | $0.87/1M tokens | $0.20/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V4 Pro | Llama 3.2 1B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V4 Pro | Llama 3.2 1B Instruct |
|---|---|---|
| MMLU PRO | 87.5 | 20.0 |
| Google-Proof Q&A | 90.1 | 25.6 |
| HumanEval | 76.8 | 28.1 |
| Massive Multitask Language Understanding | 90.1 | 49.3 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V4 Pro at 87.5 and Llama 3.2 1B Instruct at 20, with DeepSeek V4 Pro ahead by 67.5 points; Google-Proof Q&A has DeepSeek V4 Pro at 90.1 and Llama 3.2 1B Instruct at 25.6, with DeepSeek V4 Pro ahead by 64.5 points; HumanEval has DeepSeek V4 Pro at 76.8 and Llama 3.2 1B Instruct at 28.1, with DeepSeek V4 Pro ahead by 48.7 points. The largest visible gap is 67.5 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: DeepSeek V4 Pro, function calling: DeepSeek V4 Pro, and tool use: DeepSeek V4 Pro. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek V4 Pro lists $0.43/1M input and $0.87/1M output tokens on the cheapest tracked provider, while Llama 3.2 1B Instruct lists $0.03/1M input and $0.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B Instruct lower by about $0.49 per million blended tokens. Availability is 5 providers versus 7, so concentration risk also matters.
Choose DeepSeek V4 Pro when reasoning depth and larger context windows are central to the workload. Choose Llama 3.2 1B Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek V4 Pro or Llama 3.2 1B Instruct?
DeepSeek V4 Pro supports 1m tokens, while Llama 3.2 1B Instruct supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek V4 Pro or Llama 3.2 1B Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. DeepSeek V4 Pro costs $0.43/1M input and $0.87/1M output tokens. Llama 3.2 1B Instruct costs $0.03/1M input and $0.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V4 Pro or Llama 3.2 1B Instruct open source?
DeepSeek V4 Pro is listed under MIT. Llama 3.2 1B Instruct is listed under Llama 3 Community. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, DeepSeek V4 Pro or Llama 3.2 1B Instruct?
DeepSeek V4 Pro has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, DeepSeek V4 Pro or Llama 3.2 1B Instruct?
DeepSeek V4 Pro has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek V4 Pro and Llama 3.2 1B Instruct?
DeepSeek V4 Pro is available on DeepSeek Platform, Fireworks AI, OpenRouter, Vercel AI Gateway, and Novita AI. Llama 3.2 1B Instruct is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, NVIDIA NIM, and Bitdeer AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-31. Data sourced from public model cards and provider documentation.