DeepSeek V4 Flash vs GPT-5.5
At a Glance
| Compare |  DeepSeek V4 FlashDeepSeek |  GPT-5.5OpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #7 of 4683.9 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #38 of 44$0.341 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #18 of 3853.4 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.0868Openrouter ↗ · Aug 28, 2026 | $5.00Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.1736Openrouter ↗ · Aug 28, 2026 | $30.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | DeepSeek-V4-Flash | GPT-5.5 |
|---|---|---|
| ARC-AGI-1verified-v1-ba05d69f6453 · verified_score · leader | 89.0094% of row best · percent · DeepSeek V4 Flash 0731 (Max) | 95.00100% of row best · percent · GPT-5.5 (XHigh) |
| ARC-AGI-2verified-v2-6c676fa3e9af · verified_score · leader | 61.3972% of row best · percent · DeepSeek V4 Flash 0731 (Max) | 85.00100% of row best · percent · GPT-5.5 (XHigh) |
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · statistical tie | 1.8099% of row best · score · Deepseek V4 Flash (High) (20260731); 95% CI [1.06225523, 2.53222539]; sessions 64482; observations 6405377; rank 22 | 2.67100% of row best · score · GPT 5.5; 95% CI [1.85777151, 3.48040109]; sessions 81254; observations 2107641; rank 20 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,431.7998% of row best · rating · deepseek-v4-flash; 95% CI [1427.66110387, 1435.91557810]; votes 48887; rank 83 | 1,465.59100% of row best · rating · gpt-5.5; 95% CI [1461.82849712, 1469.35015015]; votes 66317; rank 31 |
| LiveBench2026-06-25 · overall · leader | 69.6782% of row best · percent · deepseek-v4-flash · 34,434 output tokens / case | 84.54100% of row best · percent · gpt-5.5-xhigh · 11,355 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 78.0094% of row best · points · DeepSeek V4 Flash (xhigh) · 12,709 output tokens / case | 82.60100% of row best · points · GPT-5.5 (xhigh) · 4,050 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 0 benchmark wins | 4 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | DeepSeek-V4-Flash | GPT-5.5 |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Family | Deepseek V4 Flash | Gpt 5 5 |
| Model | DeepSeek-V4-Flash | GPT-5.5 |
| Version | DeepSeek-V4-Flash | GPT-5.5 |
| Lifecycle | retired | active |
| Released | 2026-04-24 | 2026-04-23 |
| Knowledge cutoff | Unknown | 2025-12-01 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| Total parameters | 290.9B | Unknown |
| Active parameters | 13B | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, structured_outputs, tools |
DeepSeek V4 Flash Capabilities
GPT-5.5 Capabilities
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Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash vs GPT-5.5 FAQs
Is DeepSeek V4 Flash or GPT-5.5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash and GPT-5.5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash or GPT-5.5?+
DeepSeek V4 Flash is $0.0868 and GPT-5.5 is $5.00 per million tokens, so DeepSeek V4 Flash is cheaper on this metric. DeepSeek V4 Flash is $0.1736 and GPT-5.5 is $30.00 per million tokens, so DeepSeek V4 Flash is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Flash or GPT-5.5?+
GPT-5.5 has the larger sourced context window. DeepSeek V4 Flash supports 1,049K and GPT-5.5 supports 1,050K.
Which performs better in benchmarks, DeepSeek V4 Flash or GPT-5.5?+
GPT-5.5 leads the current overall benchmark count. The result uses 5 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can DeepSeek V4 Flash or GPT-5.5 be self-hosted?+
DeepSeek V4 Flash is the only model in this pair currently marked as self-hostable. DeepSeek V4 Flash is open weight; GPT-5.5 is not marked open weight.
Can DeepSeek V4 Flash and GPT-5.5 understand images?+
DeepSeek V4 Flash is not documented with image input; GPT-5.5 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Flash or GPT-5.5?+
Neither has a larger sourced maximum output. DeepSeek V4 Flash is — and GPT-5.5 is 128K.
Do DeepSeek V4 Flash and GPT-5.5 support reasoning and tool use?+
DeepSeek V4 Flash: reasoning. GPT-5.5: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Flash or GPT-5.5?+
DeepSeek V4 Flash has 5 sourced provider routes; GPT-5.5 has 2, so DeepSeek V4 Flash has broader tracked availability.
Which offers better value, DeepSeek V4 Flash or GPT-5.5?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.