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Head-to-head comparison

DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash

DeepSeek V4 Pro 0813 (DeepSeek) and Gemini 3.8 Flash (Google) compared on the ModelCap Index, API price, context window, provider availability, weight access and every public benchmark board they share. Figures are the same ones shown on the live rankings; nothing here is a hidden score.

Snapshot as of 24 October 2026

As of 24 October 2026, Gemini 3.8 Flash holds the stronger ModelCap Index position (#6 vs #31); DeepSeek V4 Pro 0813 is 2.1× cheaper per output token ($1.98/1M vs $4.13/1M); and DeepSeek V4 Pro 0813 ships open weights.

Which should you choose?

Choose DeepSeek V4 Pro 0813 if…

  • API cost matters — $1.98/1M output tokens against $4.13/1M, about 2.1× cheaper.
  • your workload is prompt-heavy — input tokens cost $0.66/1M against $0.825/1M.
  • you want to self-host: DeepSeek V4 Pro 0813 ships open weights while Gemini 3.8 Flash is no linked weights.
  • you want provider choice — 21 listed API providers against 2.

Choose Gemini 3.8 Flash if…

  • you want the stronger overall ModelCap Index position — #6 against #31 (82.6 vs 74.2 points).
  • Arena text is your yardstick — 1496 against 1465.
  • Arena coding is your yardstick — 1528 against 1505.
  • ARC-AGI-2 is your yardstick — 89.2% against 61.3%.
  • AA Intelligence Index is your yardstick — 40.9 against 36.0.
  • AA Terminal-Bench 2.1 is your yardstick — 87.6% against 78.7%.
  • AA Humanity's Last Exam is your yardstick — 47.8% against 41.0%.
  • you want the more recently listed model — Gemini 3.8 Flash was listed 2 September 2026, DeepSeek V4 Pro 0813 12 August 2026.

DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash: specs, pricing and context

Specification comparison of DeepSeek V4 Pro 0813 and Gemini 3.8 Flash
FieldDeepSeek V4 Pro 0813DeepSeekGemini 3.8 FlashGoogle
ModelCap Index position#31#6
Index score74.282.6
EvidenceMeasuredMeasured
Input price / 1M tokens$0.66$0.825
Output price / 1M tokens$1.98$4.13
Context window1M tokens1M tokens
Declared route output cap393K66K
API providers listed212
Weight accessOpen weightsNo linked weights
Input modalitiesTextText, Image, Video, File, Audio
First listed12 August 20262 September 2026
PublisherDeepSeekGoogle

Evidence: 3 public benchmark observations across 3 boards · 5 public benchmark observations across 5 boards. Prices are the lowest listed API offer per million tokens observed on the OpenRouter catalogue.

Benchmark scores: DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash

Public benchmark boards where DeepSeek V4 Pro 0813 or Gemini 3.8 Flash has a published result
BoardDeepSeek V4 Pro 0813Gemini 3.8 FlashLeads
Arena textArena (LMArena)14651458–1471High14961491–1500HighGemini 3.8 Flash
Arena codingArena (LMArena)15051493–1516High15281521–1535HighGemini 3.8 Flash
LMArena AgentLMArena—3.2%2.1%–4.2%HighOnly one result
ARC-AGI-2ARC Prize Foundation61.3%Max89.2%HighGemini 3.8 Flash
ARC-AGI-3ARC Prize Foundation—10.4%HighOnly one result
AA Intelligence IndexArtificial Analysis36.0Max40.9HighGemini 3.8 Flash
AA Terminal-Bench 2.1Artificial Analysis78.7%Max87.6%HighGemini 3.8 Flash
AA Humanity's Last ExamArtificial Analysis41.0%Max47.8%HighGemini 3.8 Flash
Terminal-Bench 4.0Terminal-Bench (Laude Institute)—19.1%15.7%–22.5%mini-SWE-agent · HighOnly one result

Scores are the sources' own published figures for each model's best evaluated configuration; ModelCap never re-runs a benchmark.

Want a different pairing? Open the interactive comparison tool to swap either model for any current ranked language model.

DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash: common questions

Is DeepSeek V4 Pro 0813 better than Gemini 3.8 Flash?

Gemini 3.8 Flash ranks higher on the ModelCap Index as of 24 October 2026: #6 against #31. That is a capability ranking built from public benchmark evidence with published uncertainty; whether it is "better" for you also depends on price, context and where you can run it. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.

Is DeepSeek V4 Pro 0813 cheaper than Gemini 3.8 Flash?

DeepSeek V4 Pro 0813 is cheaper on output tokens: $1.98/1M against $4.13/1M. Input tokens are $0.66/1M for DeepSeek V4 Pro 0813 and $0.825/1M for Gemini 3.8 Flash. Prices are the lowest listed API offer ModelCap observed, in USD per million tokens. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $2.31 for DeepSeek V4 Pro 0813 versus $3.71 for Gemini 3.8 Flash. DeepSeek V4 Pro 0813 costs 37.8% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.

Which has the bigger context window, DeepSeek V4 Pro 0813 or Gemini 3.8 Flash?

Both publish a 1M-token context window. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified.

Which is better for coding, DeepSeek V4 Pro 0813 or Gemini 3.8 Flash?

Arena coding: DeepSeek V4 Pro 0813 1505, Gemini 3.8 Flash 1528 — Gemini 3.8 Flash leads. AA Terminal-Bench 2.1: DeepSeek V4 Pro 0813 78.7%, Gemini 3.8 Flash 87.6% — Gemini 3.8 Flash leads.

Are DeepSeek V4 Pro 0813 and Gemini 3.8 Flash open-weight models?

DeepSeek V4 Pro 0813: Open weights. Gemini 3.8 Flash: No linked weights. Open weights mean the checkpoint can be downloaded and self-hosted under its licence. A missing weight link does not identify every API or chat product that offers a model.

Where can I run DeepSeek V4 Pro 0813 and Gemini 3.8 Flash?

ModelCap currently lists 21 API providers for DeepSeek V4 Pro 0813 and 2 for Gemini 3.8 Flash, from the OpenRouter catalogue snapshot the site serves; each model page lists the providers and their prices.

How current is this DeepSeek V4 Pro 0813 vs Gemini 3.8 Flash comparison?

Every figure and ModelCap Index position comes from the same published snapshot dated 24 October 2026. Publication and page updates happen separately; use the snapshot date shown on the page.

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