Model comparison
DeepSeek V4-Flash vs Gemini 3.6 Flash
DeepSeek against Google, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
DeepSeek
DeepSeek V4-Flash
Budget · Open weights
Gemini 3.6 Flash
Budget
| Specification | DeepSeek V4-Flash | Gemini 3.6 Flash |
|---|---|---|
| Provider | DeepSeek | |
| Tier | Budget | Budget |
| Context window | 1M | Winner: 1.05M |
| Max output | Winner: 384K | 66K |
| Input / 1M tokens | Winner: $0.14 | $0.75 |
| Output / 1M tokens | Winner: $0.28 | $3.75 |
| Weights | Open | Closed |
| Parameters | 284B total / 13B active (MoE) | Not disclosed |
| Reasoning levels | low, high, max | minimal, low, medium, high |
| Modalities | text | text, image, video, audio, pdf |
| Released | July 31, 2026 | July 21, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | 52 | 52 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: DeepSeek V4-Flash: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. · Gemini 3.6 Flash: Introductory paid-tier rates through 2026-12-31. From 2027-01-01 the standard paid rates are $1.50 / $7.50 per million tokens (Gemini API pricing). Cache is $0.075 / MTok through 2026-12-31, then $0.15. Wire calls to `gemini-3.6-flash`. Thinking levels are `minimal` / `low` / `medium` / `high` (API default medium).
DeepSeek V4-Flash
Open-weight price-performance pick: MIT weights, 1M context, and API rates far below closed budget tiers.
Best for
- Cost-sensitive hosted agents
- High-volume coding assist
- Open-weight deployments with cluster VRAM
Watch out
API list prices move to peak/off-peak from 2026-08-16 16:00 UTC (see pricing note). Self-hosting the ~284B MoE still needs roughly 90–170GB+ class VRAM depending on quant — not a laptop budget build. DeepSWE snapshot also uses the shared mini-swe-agent harness — verify cost, effort, and serving conditions before treating the result as a forecast.
Gemini 3.6 Flash
Google's July 2026 Flash tier for multimodal volume work; 3.7 Flash is the newer coding/agent workhorse at the same introductory rate.
Best for
- Multimodal pipelines
- High-volume processing
- Video and audio input
Watch out
Introductory $0.75 input is 3.75× Luna; the $1.50 rate returns on 2027-01-01. 66K max output (65,536 tokens) is roughly half Luna's. Prefer 3.7 Flash for new coding-agent work unless you are pinned to 3.6.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
DeepSeek V4-Flash vs Gemini 3.6 Flash
Answered from the verified figures on this page rather than general guidance.
Is DeepSeek V4-Flash or Gemini 3.6 Flash cheaper for input?
DeepSeek V4-Flash is cheaper at $0.14 per million input tokens, against $0.75 for Gemini 3.6 Flash — roughly 5.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4-Flash has tiered pricing: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. Gemini 3.6 Flash has tiered pricing: Introductory paid-tier rates through 2026-12-31. From 2027-01-01 the standard paid rates are $1.50 / $7.50 per million tokens (Gemini API pricing). Cache is $0.075 / MTok through 2026-12-31, then $0.15. Wire calls to `gemini-3.6-flash`. Thinking levels are `minimal` / `low` / `medium` / `high` (API default medium).
Is DeepSeek V4-Flash or Gemini 3.6 Flash cheaper for output?
DeepSeek V4-Flash is cheaper at $0.28 per million output tokens, against $3.75 for Gemini 3.6 Flash — roughly 13× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4-Flash has tiered pricing: Current list is $0.14/$0.28 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.22/$0.66, peak $0.44/$1.32 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. Gemini 3.6 Flash has tiered pricing: Introductory paid-tier rates through 2026-12-31. From 2027-01-01 the standard paid rates are $1.50 / $7.50 per million tokens (Gemini API pricing). Cache is $0.075 / MTok through 2026-12-31, then $0.15. Wire calls to `gemini-3.6-flash`. Thinking levels are `minimal` / `low` / `medium` / `high` (API default medium).
Which has the larger context window, DeepSeek V4-Flash or Gemini 3.6 Flash?
Gemini 3.6 Flash accepts 1.05M tokens against 1M for DeepSeek V4-Flash. This only matters if you routinely send very long documents or large codebases.
Do DeepSeek V4-Flash and Gemini 3.6 Flash support the same reasoning levels?
DeepSeek V4-Flash exposes low, high, max, while Gemini 3.6 Flash exposes minimal, low, medium, high.
Should I use DeepSeek V4-Flash or Gemini 3.6 Flash?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. DeepSeek V4-Flash suits cost-sensitive hosted agents; Gemini 3.6 Flash suits multimodal pipelines.
Can I self-host DeepSeek V4-Flash or Gemini 3.6 Flash?
DeepSeek V4-Flash publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.6 Flash is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.