Pick Amazon Nova Pro for multimodal input across text, image and video at $0.80/$3.20 or deep aws and bedrock integration for enterprise pipelines. Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Choose DeepSeek R1 if you need self-hosting or data privacy; Amazon Nova Pro if you want a managed API.
Amazon Nova Pro (Amazon, US) and DeepSeek R1 (DeepSeek, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Amazon Nova Pro is amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: DeepSeek R1 is about 1.5× cheaper on input ($0.55/$2.19 per 1M tokens vs $0.8/$3.2 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Amazon Nova Pro holds 2.3× more — 300K (~450 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: DeepSeek R1 is the newer model by about 46 days (released January 2025), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Amazon Nova Pro
DeepSeek R1
Provider
Amazon (US)
DeepSeek (China)
Released
December 5, 2024
January 2025
Context window
300K (~450 pages)
128K (~192 pages)
Price (in/out)
$0.8/$3.2 per 1M tokens
$0.55/$2.19 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Multimodal input across text, image and video at $0.80/$3.20: Amazon Nova Pro — Amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model — and it carries the larger 300K context.
Deep AWS and Bedrock integration for enterprise pipelines: Amazon Nova Pro — Amazon Nova Pro lists deep AWS and Bedrock integration for enterprise pipelines among its strengths; DeepSeek R1 does not.
Balanced cost-to-capability for general business tasks: Amazon Nova Pro — Amazon Nova Pro lists balanced cost-to-capability for general business tasks among its strengths; DeepSeek R1 does not.
Open-weight reasoning model: DeepSeek R1 — Open weights make this possible at all — Amazon Nova Pro is API-only, so it cannot leave the vendor's servers.
Transparent chain-of-thought: DeepSeek R1 — The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost: DeepSeek R1 — At $0.55/$2.19 per 1M tokens it undercuts Amazon Nova Pro ($0.8/$3.2 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale: DeepSeek R1 — At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Amazon Nova Pro — Its 300K window is about 2.3× larger than DeepSeek R1's 128K, fitting roughly 450 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek R1 — At $0.55/$2.19 per 1M tokens it undercuts Amazon Nova Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Amazon Nova Pro — Larger 300K window fits more in one prompt.
A team with data-privacy or self-hosting needs: DeepSeek R1 — Open weights let you run it on your own hardware; Amazon Nova Pro is API-only.
Anyone whose priority is multimodal input across text, image and video at $0.80/$3.20: Amazon Nova Pro — It is specifically built for that.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — That is its strongest area.
An enterprise with regional data-residency rules: Amazon Nova Pro or DeepSeek R1 — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Amazon Nova Pro: where it fits
Amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. Released December 5, 2024 by Amazon, it is built for multimodal input across text, image and video at $0.80/$3.20, deep AWS and Bedrock integration for enterprise pipelines, balanced cost-to-capability for general business tasks, and a 300K context for long documents and mixed media.
Its trade-offs are real: not a frontier reasoning or coding model against 2026 flagships, no published SWE-Bench Verified score, best value is realised inside the AWS ecosystem, and late-2024 model — older than most of the field here. At $0.8 in / $3.2 out per million tokens, it sits in the budget price band.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek R1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Amazon Nova Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Amazon Nova Pro or DeepSeek R1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Amazon Nova Pro leans toward multimodal input across text, image and video at $0.80/$3.20 while DeepSeek R1 leans toward open-weight reasoning model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Amazon Nova Pro or DeepSeek R1?
DeepSeek R1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Amazon Nova Pro is API-metered at $0.8/$3.2 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Amazon Nova Pro — 300K vs 128K, about 2.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Amazon Nova Pro and DeepSeek R1 together?
Yes — a multi-model platform like LumiChats gives you Amazon Nova Pro, DeepSeek R1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Amazon Nova Pro or DeepSeek R1?
DeepSeek R1 — released January 2025, about 46 days after Amazon Nova Pro.
Amazon Nova Pro vs DeepSeek R1
Amazon · US | DeepSeek · China · Updated June 2026
Quick verdict
Pick Amazon Nova Pro for multimodal input across text, image and video at $0.80/$3.20 or deep aws and bedrock integration for enterprise pipelines. Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Choose DeepSeek R1 if you need self-hosting or data privacy; Amazon Nova Pro if you want a managed API.
Amazon Nova Pro (Amazon, US) and DeepSeek R1 (DeepSeek, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Amazon Nova Pro is amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek R1 is about 1.5× cheaper on input ($0.55/$2.19 per 1M tokens vs $0.8/$3.2 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Amazon Nova Pro holds 2.3× more — 300K (~450 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: DeepSeek R1 is the newer model by about 46 days (released January 2025), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Amazon Nova Pro
DeepSeek R1
Provider
Amazon (US)
DeepSeek (China)
Released
December 5, 2024
January 2025
Context window
300K (~450 pages)
128K (~192 pages)
Price (in/out)
$0.8/$3.2 per 1M tokens
$0.55/$2.19 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Multimodal input across text, image and video at $0.80/$3.20
Amazon Nova Pro
Amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model — and it carries the larger 300K context.
Deep AWS and Bedrock integration for enterprise pipelines
Amazon Nova Pro
Amazon Nova Pro lists deep AWS and Bedrock integration for enterprise pipelines among its strengths; DeepSeek R1 does not.
Balanced cost-to-capability for general business tasks
Amazon Nova Pro
Amazon Nova Pro lists balanced cost-to-capability for general business tasks among its strengths; DeepSeek R1 does not.
Open-weight reasoning model
DeepSeek R1
Open weights make this possible at all — Amazon Nova Pro is API-only, so it cannot leave the vendor's servers.
Transparent chain-of-thought
DeepSeek R1
The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost
DeepSeek R1
At $0.55/$2.19 per 1M tokens it undercuts Amazon Nova Pro ($0.8/$3.2 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale
DeepSeek R1
At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Amazon Nova Pro
Its 300K window is about 2.3× larger than DeepSeek R1's 128K, fitting roughly 450 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek R1
At $0.55/$2.19 per 1M tokens it undercuts Amazon Nova Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Amazon Nova Pro
Larger 300K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ DeepSeek R1
Open weights let you run it on your own hardware; Amazon Nova Pro is API-only.
Anyone whose priority is multimodal input across text, image and video at $0.80/$3.20
→ Amazon Nova Pro
It is specifically built for that.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
That is its strongest area.
An enterprise with regional data-residency rules
→ Amazon Nova Pro or DeepSeek R1
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Amazon Nova Pro: where it fits
Amazon's multimodal Bedrock model at $0.80/$3.20 with a 300K window — an enterprise-integrated generalist, not a frontier model. Released December 5, 2024 by Amazon, it is built for multimodal input across text, image and video at $0.80/$3.20, deep AWS and Bedrock integration for enterprise pipelines, balanced cost-to-capability for general business tasks, and a 300K context for long documents and mixed media.
Its trade-offs are real: not a frontier reasoning or coding model against 2026 flagships, no published SWE-Bench Verified score, best value is realised inside the AWS ecosystem, and late-2024 model — older than most of the field here. At $0.8 in / $3.2 out per million tokens, it sits in the budget price band.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek R1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Amazon Nova Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Amazon Nova Pro and DeepSeek R1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Amazon Nova Pro or DeepSeek R1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Amazon Nova Pro leans toward multimodal input across text, image and video at $0.80/$3.20 while DeepSeek R1 leans toward open-weight reasoning model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Amazon Nova Pro or DeepSeek R1?
DeepSeek R1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Amazon Nova Pro is API-metered at $0.8/$3.2 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Amazon Nova Pro — 300K vs 128K, about 2.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Amazon Nova Pro and DeepSeek R1 together?
Yes — a multi-model platform like LumiChats gives you Amazon Nova Pro, DeepSeek R1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Amazon Nova Pro or DeepSeek R1?
DeepSeek R1 — released January 2025, about 46 days after Amazon Nova Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.