Head-to-head
GPT-5.5 vs DeepSeek-V4: which AI model wins in 2026?
GPT-5.5 ($30/1M out) and DeepSeek-V4 ($0.87/1M out) are two of the most-used AI models in 2026. Across 6 community votes, GPT-5.5 leads with 57% approval.
Quick verdict
On Reasoning, pick GPT-5.5: the arena rates it 5/5 against 4.5/5 for DeepSeek-V4. On budget, DeepSeek-V4 wins: it starts at $0.87/1M out versus $30/1M out for GPT-5.5.
Line-by-line comparison
Strengths and weaknesses
GPT-5.5
- 1M-token context window (1,050,000) with 128K max output and reasoning effort tunable from none to xhigh
- State-of-the-art ARC-AGI-2 at 85.0% (vs 73.3% for GPT-5.4) and Terminal-Bench 2.0 at 82.7%
- Strong agentic coding autonomy: devs report it one-shots tasks that took GPT-5.4 multiple turns and fixes its own mistakes; +50 points on Code Arena vs GPT-5.4
- Aggressive discounts: 90% off cached input ($0.50/1M) and 50% off via Batch or Flex ($2.50/$15)
- Fast for a frontier reasoner: devs say it is the first GPT model comfortable to run at medium or low thinking effort
- List price doubled vs GPT-5.4 ($5/$30 vs $2.50/$15) for the same 1M-token context window
- Overly literal instruction-following: devs report it fails to infer intent in obvious places where Claude succeeds
- Trails Claude Opus 4.8 on SWE-bench Pro (58.6% vs 69.2%); HN developers still favor Claude roughly 2:1 for coding
- Sometimes too conservative with code changes or skips deep reasoning entirely, answering immediately on complex prompts
- Long-context surcharge: prompts over 272K input tokens are billed 2x input and 1.5x output for the whole session
DeepSeek-V4
- 1M-token context window (8x the 128K of V3.2) with up to 384K output tokens, standard on the official API
- Aggressive pricing: $0.435/$0.87 per 1M tokens (V4-Pro), roughly 28.7x cheaper per output token than Claude Opus 4.8; cache-hit input drops to $0.003625/1M (over 99% discount)
- MIT-licensed open weights for both V4-Pro and V4-Flash on Hugging Face: commercial use, fine-tuning and redistribution allowed
- Open-source SOTA on agentic coding: 80.6 on SWE-bench Verified (Think Max config), tied with Gemini 3.1 Pro, plus Codeforces rating 3206 (~rank 23 vs humans)
- Ranks #3 of 93 on the Artificial Analysis Intelligence Index (score 44), well above the 25 average
- Sparse-attention stack cuts 1M-context inference to 27% of V3.2's FLOPs and 10% of its KV cache
- Intermittent malformed tool calls: function calls sometimes emitted as plain text in content instead of the tool_calls field (GitHub issue deepseek-ai #1244)
- Thinking mode breaks long multi-turn tool-call chains with 400 errors in agent frameworks (OpenClaw issue #72044, fix still incomplete)
- Developers report it fabricating nonexistent APIs in custom codebases and acting on hallucinated user input in agent loops
- Very verbose (180M eval output tokens vs 95M median) and mid-pack speed at 54.6 tok/s (#39/93), which erodes the low per-token price in practice
- Text only (no vision or audio) and still a preview: the official release planned for mid-July 2026 adds peak-hour pricing that doubles listed API rates during Beijing business hours
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on GPT-5.5
Pick GPT-5.5 over GPT-5.4 if you need stronger agentic autonomy, terminal-heavy workflows, or SOTA abstract reasoning, but know the list price doubled from GPT-5.4's $2.50/$15 to $5/$30 while the 1M-token context stayed the same. Teams doing high-stakes multi-file refactoring may still prefer Claude Opus, which leads SWE-bench Pro (69.2% vs 58.6%) and infers intent better from loose prompts. Budget-sensitive users should mind the 272K-token surcharge and reports of faster limit burn, and lean on caching, Batch, or Flex to halve costs.
The arena’s verdict on DeepSeek-V4
Choose DeepSeek-V4 if you want near-frontier reasoning and agentic coding at 3x to nearly 30x below Claude Opus or GPT-5.5 pricing, or if MIT-licensed weights for self-hosting and fine-tuning matter to you. It is a decisive upgrade over V3.2: 8x longer context, far cheaper long-context inference and stronger coding, and the legacy deepseek-chat/reasoner endpoints are deprecated on July 24, 2026 anyway. Avoid it for production agents that depend on rock-solid multi-turn tool calling, where users still report malformed tool calls and fabricated APIs, and for any vision or audio work since it is text only. Latency-sensitive apps should also test first, as its verbosity and mid-pack 54.6 tok/s output speed offset some of the cost advantage, and budget for the peak-hour price doubling arriving with the official mid-July release.
What the crowd says
On GPT-5.5
“It is painfully literal. Where Claude infers intent in obvious places, 5.5 wants everything spelled out. And the price doubled vs 5.4 for the same 1M context.”
“85 on ARC-AGI-2 and you can feel it. Stuff that used to stall my agent just resolves now. 1M context with 128K output covers every workflow I have.”
“5.5 one-shots tasks that took 5.4 three turns, and it fixes its own mistakes mid-run instead of doubling down. The reasoning effort dial from none to xhigh is genuinely useful.”
On DeepSeek-V4
“Tool calling is flaky. Function calls sometimes land as plain text instead of the tool_calls field, and thinking mode 400s on long multi-turn chains. Not agent-ready yet.”
“MIT license on both Pro and Flash weights is the real story. Fine-tune, redistribute, ship commercially, no lawyer needed. Plus 384K output tokens for long-doc generation.”
“MIT weights, 1M context, and output tokens roughly 29x cheaper than Opus 4.8. Cache hits make input basically free. Moved my bulk pipelines over and the bill collapsed.”
Keep comparing
Frequently asked questions
Is GPT-5.5 better than DeepSeek-V4?
On Reasoning, GPT-5.5 rates higher (5/5 vs 4.5/5). The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, GPT-5.5 or DeepSeek-V4?
DeepSeek-V4 is cheaper: it starts at $0.87/1M out, while GPT-5.5 starts at $30/1M out.
How much do GPT-5.5 and DeepSeek-V4 cost per 1M tokens?
GPT-5.5: $5/1M in per 1M input tokens, $30/1M out per 1M output tokens. DeepSeek-V4: $0.435/1M in (cache hit $0.003625) per 1M input tokens, $0.87/1M out per 1M output tokens.