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 shared |
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 and JSON / 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 route 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 and JSON / 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 and JSON / 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 | Decoder Only |
| License | MITOSI-approved | Llama 3 Community |
| Openness | Open source | Open weights |
| Weights | Available | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| 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 |
| JSON / 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 |
Continue comparing
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.