Nova 2 Lite vs DeepSeek V4 Pro
Benchmark Performance
Available Benchmarks
| Benchmark | Nova 2 Lite | DeepSeek-V4-Pro |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,362.5294% of row best · rating · nova-2-lite; 95% CI [1356.44886287, 1368.59662424]; votes 12117; rank 178 | 1,451.00100% of row best · rating · deepseek-v4-pro; 95% CI [1447.00829245, 1454.98663953]; votes 54243; rank 41 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 1 benchmark winOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Nova 2 Lite | DeepSeek-V4-Pro |
|---|---|---|
| Developer | Amazon | DeepSeek |
| Family | Nova 2 | Deepseek V4 Pro |
| Model | Nova 2 Lite | DeepSeek-V4-Pro |
| Version | Nova 2 Lite | DeepSeek-V4-Pro |
| Lifecycle | active | active |
| Released | 2025-12-02 | 2026-04-24 |
| Knowledge cutoff | 2025-10-01 | Unknown |
| Input modalities | Text, Image, Video, Document | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 1,049K |
| Total parameters | Unknown | 1.6T |
| Active parameters | Unknown | 49B |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Amazon Bedrock (Standard) | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, prompt-caching, tools, vision | chat, generation, reasoning |
14 comparable fields · 11 material differences · Pair passes the primary-source comparison gate
Nova 2 Lite Capabilities
DeepSeek V4 Pro Capabilities
Internal Comparison Graph
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|---|---|---|---|
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Primary Evidence
Sources and Freshness
Questions
Nova 2 Lite vs DeepSeek V4 Pro FAQs
Is Nova 2 Lite or DeepSeek V4 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Nova 2 Lite and DeepSeek V4 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Nova 2 Lite or DeepSeek V4 Pro?+
Nova 2 Lite is $0.30 and DeepSeek V4 Pro is $0.66 per million tokens, so Nova 2 Lite is cheaper on this metric. Nova 2 Lite is $2.50 and DeepSeek V4 Pro is $1.5441 per million tokens, so DeepSeek V4 Pro is cheaper on this metric.
Which has a larger context window, Nova 2 Lite or DeepSeek V4 Pro?+
DeepSeek V4 Pro has the larger sourced context window. Nova 2 Lite supports 1,000K and DeepSeek V4 Pro supports 1,049K.
Which performs better in benchmarks, Nova 2 Lite or DeepSeek V4 Pro?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Nova 2 Lite or DeepSeek V4 Pro be self-hosted?+
DeepSeek V4 Pro is the only model in this pair currently marked as self-hostable. Nova 2 Lite is not marked open weight; DeepSeek V4 Pro is open weight.
Can Nova 2 Lite and DeepSeek V4 Pro understand images?+
Nova 2 Lite is documented with image input; DeepSeek V4 Pro is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Nova 2 Lite or DeepSeek V4 Pro?+
Neither has a larger sourced maximum output. Nova 2 Lite is 66K and DeepSeek V4 Pro is —.
Do Nova 2 Lite and DeepSeek V4 Pro support reasoning and tool use?+
Nova 2 Lite: tool calling and image input. DeepSeek V4 Pro: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Nova 2 Lite or DeepSeek V4 Pro?+
Nova 2 Lite has 1 sourced provider route; DeepSeek V4 Pro has 6, so DeepSeek V4 Pro has broader tracked availability.
Which offers better value, Nova 2 Lite or DeepSeek V4 Pro?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.