DeepSeek V3.2 vs Llama 4 Maverick 17B Instruct FP8
DeepSeek V3.2 (2025) and Llama 4 Maverick 17B Instruct FP8 (2025) are general-purpose language models from DeepSeek and AI at Meta. DeepSeek V3.2 ships a 160k-token context window, while Llama 4 Maverick 17B Instruct FP8 ships a 1m-token context window. On Google-Proof Q&A, DeepSeek V3.2 leads by 16.9 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 4 Maverick 17B Instruct FP8 is ~68% cheaper at $0.15/1M; pay for DeepSeek V3.2 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.2 | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Best for | provider-routed production | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 160k | 1m |
| Cheapest output | $0.38/1M tokens | $0.60/1M tokens |
| Provider routes | 7 tracked | 10 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 1 shared |
Decision tradeoffs
- DeepSeek V3.2 holds a shared-benchmark lead on Google-Proof Q&A, ahead by 16.9 points.
- DeepSeek V3.2 has the lower cheapest tracked output price at $0.38/1M tokens.
- DeepSeek V3.2 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.2 for Coding, RAG, and Agents.
- Llama 4 Maverick 17B Instruct FP8 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Maverick 17B Instruct FP8 has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B Instruct FP8 uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Llama 4 Maverick 17B Instruct FP8 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.2
$296
Cheapest tracked route/tier: OpenRouter
Llama 4 Maverick 17B Instruct FP8
$270
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $26.10. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Microsoft Foundry, OpenRouter, and Fireworks AI; start route-level A/B tests there.
- Llama 4 Maverick 17B Instruct FP8 is $0.22/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution before moving production traffic.
- Llama 4 Maverick 17B Instruct FP8 adds Vision and Multimodal in local capability data.
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and AWS Bedrock; start route-level A/B tests there.
- DeepSeek V3.2 is $0.22/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- DeepSeek V3.2 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2025-04-05 |
| Context window | 160k | 1m |
| Parameters | 671B | 400B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | MITOSI-approved | Llama 4 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | - | 2024-08 |
Pricing and availability
| Pricing attribute | DeepSeek V3.2 | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Input price | $0.25/1M tokens | $0.15/1M tokens |
| Output price | $0.38/1M tokens | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.2 | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.2 | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Google-Proof Q&A | 84.0 | 67.1 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek V3.2 at 84 and Llama 4 Maverick 17B Instruct FP8 at 67.1, with DeepSeek V3.2 ahead by 16.9 points. The largest visible gap is 16.9 points on Google-Proof Q&A, 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 vision: Llama 4 Maverick 17B Instruct FP8, multimodal input: Llama 4 Maverick 17B Instruct FP8, and code execution: DeepSeek V3.2. 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 V3.2 lists $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider, while Llama 4 Maverick 17B Instruct FP8 lists $0.15/1M input and $0.60/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Maverick 17B Instruct FP8 lower by about $0.00 per million blended tokens. Availability is 7 providers versus 10, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support are central to the workload. Choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis, larger context windows, and lower input-token cost 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 V3.2 or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 supports 1m tokens, while DeepSeek V3.2 supports 160k 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 V3.2 or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.25/1M input and $0.38/1M output tokens. Llama 4 Maverick 17B Instruct FP8 costs $0.15/1M input and $0.60/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Llama 4 Maverick 17B Instruct FP8 open source?
DeepSeek V3.2 is listed under MIT. Llama 4 Maverick 17B Instruct FP8 is listed under Llama 4 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 vision, DeepSeek V3.2 or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, DeepSeek V3.2 or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek V3.2 and Llama 4 Maverick 17B Instruct FP8?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.