DeepSeek V4.1 Flash vs Gemini 3.7 Flash
Close to a tie overall. DeepSeek V4.1 Flash costs 80% less for the same work.
DeepSeek V4.1 Flash
by DeepSeek · #11 of 67 overall
- Context
- 1M
- Input / 1M
- $0.20
- Output / 1M
- $0.60
Gemini 3.7 Flash
by Google · #11 of 67 overall
- Context
- 1M
- Input / 1M
- $0.75
- Output / 1M
- $3.75
Better overall
About even
Grade 83.5 vs 83.5
Cheaper
DeepSeek V4.1 Flash
80% less, $0.30 vs $1.50 blended
Larger context
DeepSeek V4.1 Flash
1M vs 1M tokens
Terminal-Bench 3.0
DeepSeek V4.1 Flash
30% vs 14.9%
Head to head
Public benchmarks. Longer is better; each value links to its source.
DeepSeek V4.1 FlashGemini 3.7 Flashv = reported by the model’s maker. — = no public score yet. Arena bars span the range across every model we grade.
Speed through FullsendAI
Measured through FullsendAI, from one location, Oct 15, 2026, n=20 (5 for long runs).
DeepSeek V4.1 FlashGemini 3.7 Flash
Time to first tokenmedian, tick at p90 · lower is faster
Output speed1,500-token replies, tick at p90
First-try success 55/55 for DeepSeek V4.1 Flash and — for Gemini 3.7 Flash. FullsendAI retries busy responses automatically, so a missed first try costs a moment, not an error.
How we measured
Streamed chat completions with default settings, the two models alternating run by run. Time to first token runs from sending the request to the first streamed token, connection included, over short prompts. Output speed is output tokens a second after the first token, on long replies. One location and one afternoon, so treat small differences as noise.
What it costs you
Drag to match your monthly usage.
DeepSeek V4.1 Flash saves you $236 a month at this usage, $2,832 a year.
Capabilities
| Capability | DeepSeek V4.1 Flash | Gemini 3.7 Flash |
|---|---|---|
| Reasoning levels through FullsendAI | Yes | Yes |
| Reads images through FullsendAI | Yes | Yes |
| Open weights | Yes | — |
| Tool calling | Yes | — |
| JSON output | Yes | — |
| Tool calls that worked (measured) | 10/10 | — |
| Valid JSON replies (measured) | 10/10 | — |
Our coding quick check (ten small Python functions against hidden tests; not a benchmark): 10/10 for DeepSeek V4.1 Flash, — for Gemini 3.7 Flash.
Maker-published benchmarks
As each maker reports them, with their own harnesses and settings: not graded, and not strictly comparable across makers.
| Benchmark | DeepSeek V4.1 Flash | Gemini 3.7 Flash |
|---|---|---|
| Terminal-Bench 2.1 | 90.6% | — |
| Terminal-Bench 3.0 | 30% | — |
| Terminal-Bench 4.0 | 31.2% | — |
| DeepSWE v1.1 | 74.2% | — |
| NL2Repo-Bench | 64 | — |
| ProgramBench | 20.3% | — |
| Codeforces | 3,471 rating | — |
| SEC-Bench Pro | 62.8% | — |
| CyberGym | 88.1% | — |
| ExploitGym | 15.3% | — |
| HumanEval (base model) | 79.4% | — |
| BigCodeBench (base model) | 60.6% | — |
| AutomationBench | 54.8% | — |
| Agent's Last Exam | 31.8% | — |
Each score links to where its maker published it.
At a glance
| Attribute | DeepSeek V4.1 Flash | Gemini 3.7 Flash |
|---|---|---|
| Made by | DeepSeek | |
| Availability | Coming soon | Coming soon |
| Overall grade | Excellent · 83.5 | Excellent · 83.5 |
| Overall rank | #11 of 67 | #11 of 67 |
| Input price | $0.20 per 1M | $0.75 per 1M |
| Output price | $0.60 per 1M | $3.75 per 1M |
| Context window | 1,048,576 tokens | 1,000,000 tokens |
| Reasoning levels | Yes | Yes |
| Reads images | Yes | Yes |
| Model ID | deepseek-v4.1-flash | gemini-3.7-flash |
Model card: what the makers publish
| Fact | DeepSeek V4.1 Flash | Gemini 3.7 Flash |
|---|---|---|
| Released | Sep 10, 2026 | — |
| Made by | DeepSeek (DeepSeek-AI) | — |
| Licence | MIT License (repository and model weights) | — |
| Commercial use | Allowed (MIT License permits use, modification, distribution, sublicensing and sale) | — |
| Open weights | Yes | — |
| Parameters | 552B backbone parameters (includes 196B Engram conditional-memory parameters per the card's architecture notes) | — |
| Active parameters | 8B per token during prefill / 16B per token during decode | — |
| Mixture of experts | Yes: 1 shared + 384 routed experts per MoE layer, 6 routed experts active per token | — |
| Architecture | Causal Encoder-Decoder (CED): 40-layer Transformer = 20-layer causal encoder + 20-layer decoder; decoder global KV projected from final encoder states. Compressed Sparse Attention 2 (CSA2, Full/Reindex/Reuse layer modes, Hierarchical Sparse Indexer), FP4 main KV cache (E2M1, one E4M3 scale per 16 channels), ~890 bytes/token global KV cache (~1/4 of DeepSeek-V4-Flash), SWA Bounded Replay, Single-Pass mHC, Engram conditional memory (196B params), DSpark speculative decoding, DeepSeek-ViT vision encoder + 2-layer MLP projector. | — |
| Context window | 1,048,576 tokens | — |
| Max output | 384K (DeepSeek's own API, 'MAXIMUM: 384K') | — |
| Input | Text, Image | — |
| Output | Text | — |
| Reasoning | Thinking mode with continuously controllable reasoning effort (integer 1-100). DeepSeek API: thinking on by default (effort 'high'); toggle via {"thinking": {"type": "enabled"|"disabled"}}, effort via reasoning_effort low/high/max. In thinking mode temperature/penalties are ignored; reasoning returned in reasoning_content. | — |
| Tool calling | Yes | — |
| JSON output | JSON Output via response_format {'type':'json_object'} (prompt must mention 'json') | — |
| Recommended sampling | temperature 1.0, top_p 0.95 or 1.0, context_window 1M, max_tokens >= 256K | — |
| Training data | Pre-trained from scratch on a 45T-token multimodal corpus; context extended to 1M at 34T tokens | — |
| Other features | Chat Prefix Completion (Beta), FIM Completion (Beta, non-thinking mode only), Responses API and Anthropic-format API on DeepSeek's own endpoint; context caching (cache-hit pricing); vision | — |
Sources
- Released: DeepSeek V4.1 Flash
- Made by: DeepSeek V4.1 Flash
- Licence: DeepSeek V4.1 Flash
- Commercial use: DeepSeek V4.1 Flash
- Open weights: DeepSeek V4.1 Flash
- Parameters: DeepSeek V4.1 Flash
- Active parameters: DeepSeek V4.1 Flash
- Mixture of experts: DeepSeek V4.1 Flash
- Architecture: DeepSeek V4.1 Flash
- Context window: DeepSeek V4.1 Flash
- Max output: DeepSeek V4.1 Flash
- Input: DeepSeek V4.1 Flash
- Output: DeepSeek V4.1 Flash
- Reasoning: DeepSeek V4.1 Flash
- Tool calling: DeepSeek V4.1 Flash
- JSON output: DeepSeek V4.1 Flash
- Recommended sampling: DeepSeek V4.1 Flash
- Training data: DeepSeek V4.1 Flash
- Other features: DeepSeek V4.1 Flash
Try both with one key
Same endpoint, same key, both hosted in the USA. Change one word to switch models, in Claude Code, Cursor, Cline or your own code.
Get your API key# Ask either model the same thing curl https://fullsendai.com/v1/chat/completions \ -H "Authorization: Bearer $FULLSEND_API_KEY" \ -d '{"model": "deepseek-v4.1-flash", "messages": [...]}' # …then swap one word -d '{"model": "gemini-3.7-flash", "messages": [...]}'
Questions
Is DeepSeek V4.1 Flash or Gemini 3.7 Flash better?
They grade within two points of each other (83.5 and 83.5), so pick on price and the benchmarks that match your work.
Which is cheaper, DeepSeek V4.1 Flash or Gemini 3.7 Flash?
DeepSeek V4.1 Flash: $0.20 in and $0.60 out per million tokens, against $0.75 and $3.75 through FullsendAI.
Which has the larger context window?
DeepSeek V4.1 Flash, with 1,048,576 tokens.
Can I use both with one API key?
Yes, once they launch: both are coming soon, at the prices shown, and both will use the same FullsendAI key.
Compare with others
Scores: LMArena (CC BY 4.0), Terminal-Bench, and model makers’ published results where marked v. Prices are FullsendAI’s, per million tokens. Data as of Oct 28, 2026.