Skip to main content
AI Choice Engine

Model comparison

Amazon Nova 2 Pro vs DeepSeek V4 Flash

Amazon against DeepSeek, compared on context, price, and attributed benchmark results.

Amazon Nova 2 Pro: profile checked October 25, 2026DeepSeek V4 Flash: profile checked October 25, 2026Individual provider fields may changeEvidence confidence: Insufficient data — see receipts below

Amazon

Amazon Nova 2 Pro

Balanced

vs

DeepSeek

DeepSeek V4 Flash

Balanced · Open weights

AI model capability comparison
SpecificationAmazon Nova 2 ProDeepSeek V4 Flash
ProviderAmazonDeepSeek
TierBalancedBalanced
Context window1M1M
Max outputNot verifiedUnverifiedNot verifiedUnverified
Input / 1M tokens$1.25Not verifiedUnverified
Output / 1M tokens$10Not verifiedUnverified
WeightsClosedOpen
ParametersNot verifiedUnverified284B total / 13B active (MoE)
Reasoning levelslow, medium, highlow, high, max
Modalitiestext, image, video, audiotext
LicenseNot verifiedUnverifiedMIT
API model idNot verifiedUnverifieddeepseek-v4-flash
ReleasedDecember 2, 2025April 24, 2026
SWE-bench Verified · source date DeepSeek V4 Flash: 2026-04-24Not verifiedUnverified79
GPQA Diamond · source date DeepSeek V4 Flash: 2026-04-24Not verifiedUnverified88.1
Humanity's Last Exam · source date DeepSeek V4 Flash: 2026-04-24Not verifiedUnverified45.1
MMLU-Pro · source date DeepSeek V4 Flash: 2026-04-24Not verifiedUnverified86.2

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, exact match)

    Broad knowledge across 14 domains with harder, reasoning-focused multiple-choice questions.

    Comparable with caveatFrontier models cluster in the high 80s–90s; near-ceiling differences are weak evidence. CoT vs direct answering must match.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, with tools; 34.8 no tools)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

  • Source date2026-04-24
    DeepSeek family HF model card (vendor, Think Max, pass@1)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-04-24
    DeepSeek original family HF model card (vendor, Think Max; historical April checkpoint, not the 0731 refresh)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers

Amazon Nova 2 Pro: Bedrock Global Standard $1.25/$10 per MTok; single-region $1.375/$11. Global Flex/Batch $0.625/$5 and regional $0.6875/$5.50. Global Priority $2.1875/$17.50 and regional $2.40625/$19.25. These are native Nova 2.0 Pro on-demand meters in the complete offer published October 16, 2026, with October 1 effective dates; not custom-model, provisioned-throughput or training prices. No Pro cached-input meter is present in this capture. Billing meters do not establish GA or account access.

DeepSeek V4 Flash: Retired 2026-09-10 — no longer sold as the original checkpoint. Historical checkpoint tariffs are not verified here. The legacy deepseek-v4-flash ID now routes to V4.1-Flash-1017 at $0.30/$1.20 peak or $0.15/$0.60 off-peak, with $0.006/$0.003 cache reads. MIT weights remain. The 384K current API output ceiling and vision support belong to the routed revision, not these original text-only weights.

BalancedRecord checked October 25, 2026

Amazon Nova 2 Pro

Amazon's closed Nova 2 Pro preview accepts text, images, video and speech and generates text; its launch announcement specifies a one-million-token context.

Best for

  • AWS-native workloads
  • Multimodal
  • Long-context

Watch out

Preview access was announced for Nova Forge customers through their AWS account team. Current account access, a stable API model ID and a hard output cap remain unverified. Launch documentation names low/medium/high thinking; the current detailed thinking guide covers Lite only, so do not transfer its request rules to Pro. The October 1 Artificial Analysis 14-point Pro row is an estimate and is withheld. Omission from a current model list does not prove retirement.

BalancedOpen weightsRecord checked October 25, 2026

DeepSeek V4 Flash

DeepSeek V4 Flash was the efficient open-weight (MIT) sibling of V4 Pro — retired from DeepSeek's API on 2026-09-10 and replaced by V4.1-Flash.

Best for

  • Open-weight deployments
  • Cost-sensitive inference
  • High-volume serving

Watch out

The original V4 Flash API checkpoint retired September 10; its old ID is still accepted and now serves Flash-1017. Original MIT weights remain available for self-hosting. Original API low/high/max settings are historical controls, not evidence of a matching native hosted menu or successor performance.

When the cheaper one wins

One of these rows is missing a verified output price, so this page will not name a cheaper winner. Run the model picker against the actual job instead of guessing.

Run the model picker

Evidence confidence: Insufficient data

How strong and complete the evidence behind this comparison is — not a prediction of which model is better. Hard limitations apply: missing pricing; evidence too sparse.

  • Pricing incomplete — No verified token pricing for DeepSeek V4 Flash.
  • 3/5 core specs verified on both sides — Not published for at least one side: max output, parameter count.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 1 day ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? Amazon Nova 2 Pro · DeepSeek V4 Flash

Common questions

Amazon Nova 2 Pro vs DeepSeek V4 Flash

Answered from the verified figures on this page rather than general guidance.

Which has the larger context window, Amazon Nova 2 Pro or DeepSeek V4 Flash?
Both accept about 1M tokens of context, so document length will not decide between them.
Do Amazon Nova 2 Pro and DeepSeek V4 Flash support the same reasoning levels?
Amazon Nova 2 Pro exposes low, medium, high, while DeepSeek V4 Flash exposes low, high, max.
Should I use Amazon Nova 2 Pro or DeepSeek V4 Flash?
Both are classified in our balanced tier; this editorial grouping does not imply equal capability. Amazon Nova 2 Pro is a candidate for aws-native workloads; DeepSeek V4 Flash is a candidate for open-weight deployments. Compare accuracy, tool reliability, latency and cost on representative tasks.
Can I self-host Amazon Nova 2 Pro or DeepSeek V4 Flash?
DeepSeek V4 Flash publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Amazon Nova 2 Pro is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Deployment: DeepSeek V4 Flash publishes weights you can self-host; the other is API-only.
Run the model pickerEstimate stack costAll model comparisons

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.