Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

DeepSeek V4 Flash Latest vs Gemini 1.5 Pro benchmark

Name: DeepSeek V4 Flash Latest vs Gemini 1.5 Pro AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek V4 Flash Latest (~deepseek) and Gemini 1.5 Pro (Google) using the latest snapshots we have as of Aug 17, 2026. DeepSeek V4 Flash Latest wins on output price at $0.12/1M. Gemini 1.5 Pro has the larger context window (2M tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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~deepseek

DeepSeek V4 Flash Latest

Auto-discovered from OpenRouter (~deepseek/deepseek-v4-flash-latest). Preview until a second source matches.

$0.12060000000000001/1M out

Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V4 Flash Latest (2)vsGemini 1.5 Pro (7)

DeepSeek V4 Flash Latest: 2W (22%)Overall: Gemini 1.5 ProGemini 1.5 Pro: 7W (78%)

← DeepSeek V4 Flash LatestGemini 1.5 Pro →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,260

Coding Elo

—vs—

SWE-bench

—vs38%

LiveBench

—vs49.2%

GPQA Diamond

—vs58%

DeepSeek V4 Flash Latest: 0WGemini 1.5 Pro: 4W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs70 tok/s

Time to first token

—vs240 ms

DeepSeek V4 Flash Latest: 0WGemini 1.5 Pro: 2W

DeepSeek V4 Flash Latest (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.12/1Mvs$5/1M

Input price

$0.06/1Mvs$1.25/1M

Context window

1.3Mvs2M

DeepSeek V4 Flash Latest: 2WGemini 1.5 Pro: 1W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGemini 1.5 Pro 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

DeepSeek V4 Flash Latest

$4.88/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,260

LiveBench

High is better

—

LiveBenchHigh is better

49.2%

SWE-bench

High is better

—

SWE-benchHigh is better

38%

GPQA

High is better

—

GPQAHigh is better

58%

TTFT

Low is better

—

TTFTLow is better

240 ms

Speed

High is better

—

SpeedHigh is better

70 tok/s

In $

Low is better

+1973%$0.06/1M

In $Low is better

$1.25/1M

Out $

Low is better

+4046%$0.12/1M

Out $Low is better

$5/1M

Context

High is better

1.3M

ContextHigh is better

2M+53%

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 97% with DeepSeek V4 Flash Latest

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

DeepSeek V4 Flash Latest$3.92 / mo

In: $2.11Out: $1.81

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75.00

Estimated Cost Delta

DeepSeek V4 Flash Latest is estimated to save $114.83/month ($1,378/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek V4 Flash Latest:Pick DeepSeek V4 Flash Latest when you are optimizing output cost.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatDeepSeek V4 Flash Latest

Lower output list price ($0.12/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	DeepSeek V4 Flash Latest	Lower output list price ($0.12/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Community Sentiment

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CompareLLM Matrix

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LIVE

~deepseek

DeepSeek V4 Flash Latest

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.12/1M

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

Frequently Asked Questions

Which is better overall, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: DeepSeek V4 Flash Latest output tokens are $0.12/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 1.3M.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek V4 Flash Latest and Gemini 1.5 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek V4 Flash Latest matchups →|All Gemini 1.5 Pro matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)Gemini 3 Pro vs Gemini 1.5 Pro Gemini 3 Flash vs Gemini 1.5 Pro Gemini 2.0 Flash Thinking vs Gemini 1.5 Pro Gemini 3.6 Pro vs Gemini 1.5 Pro Gemini 3.6 Flash vs Gemini 1.5 Pro

DeepSeek V4 Flash Latest vs Gemini 1.5 Pro benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V4 Flash Latest (2)vsGemini 1.5 Pro (7)

DeepSeek V4 Flash Latest: 2W (22%)Overall: Gemini 1.5 ProGemini 1.5 Pro: 7W (78%)

← DeepSeek V4 Flash LatestGemini 1.5 Pro →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,260

Coding Elo

—vs—

SWE-bench

—vs38%

LiveBench

—vs49.2%

GPQA Diamond

—vs58%

DeepSeek V4 Flash Latest: 0WGemini 1.5 Pro: 4W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs70 tok/s

Time to first token

—vs240 ms

DeepSeek V4 Flash Latest: 0WGemini 1.5 Pro: 2W

DeepSeek V4 Flash Latest (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.12/1Mvs$5/1M

Input price

$0.06/1Mvs$1.25/1M

Context window

1.3Mvs2M

DeepSeek V4 Flash Latest: 2WGemini 1.5 Pro: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatDeepSeek V4 Flash Latest

Lower output list price ($0.12/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	DeepSeek V4 Flash Latest	Lower output list price ($0.12/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

DeepSeek V4 Flash Latest output tokens are $0.12/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V4 Flash Latest or Gemini 1.5 Pro?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 1.3M.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek V4 Flash Latest and Gemini 1.5 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.