Three frontier models compared head-to-head: coding, reasoning, long-context, Chinese, pricing.
One-line conclusion: V4 leads open-source coding + Chinese + price (1/10 of Claude); GPT-5.5 wins on math + multimodal; Claude leads long-chain reasoning stability + ecosystem maturity. These three complement rather than replace each other.
Latest public data as of 2026.
| Dimension | DeepSeek V4-Pro | GPT-5.5 | Claude Opus 4.6 |
|---|---|---|---|
| Vendor | DeepSeek（中国） | OpenAI（美国） | Anthropic（美国） |
| Total / Activated | 1.6T / 49B | Undisclosed | Undisclosed |
| Context window | 1,000,000 tokens | 400,000 tokens | 200,000 tokens (4.7: 1M) |
| Max output | 384K | 128K | 128K |
| Multimodal | Text + Image | Text + Image + Audio + Video | Text + Image |
| License | MIT open source | Closed | Closed |
| Input price / MTok | $0.435 | $1.25 | $15.00 |
| Output price / MTok | $0.87 | $10.00 | $75.00 |
| Thinking mode | Yes (high/max) | Yes (router) | Yes (extended) |
Source: DeepSeek official docs, Anthropic, OpenAI, pricepertoken.com, benchlm.ai — May 2026 data. V4 is MIT licensed; local deployment has zero license cost.
V4 leads the open-source camp; GPT-5.5 and Claude 4.6 each have their strengths on the closed-source side.
| Benchmark | Category | DeepSeek V4-Pro-Max | GPT-5.5 | Claude Opus 4.6 |
|---|---|---|---|---|
| LiveCodeBench | Live Coding | 93.5% | ~90% | ~88% |
| Codeforces Rating | Competitive Programming | 3206 | 3168 | ~3000 |
| SWE-bench Verified | Real Software Engineering | 80.6% | ~80% | 80.8% |
| HumanEval pass@1 | Code Generation | 90.8% | 90.2% | ~88% |
| AIME 2026 | Math Competition | 99.4% | ~99% | ~98% |
V4-Pro-Max leads on LiveCodeBench, Codeforces, and HumanEval among open-source models (and exceeds some closed-source models), with SWE-bench just 0.2 points behind Claude 4.6. But one third-party test (38 tasks) showed V4 completed 29/38 (76%) while Claude completed 38/38 (100%) — V4 averages higher but Claude is more reliable on the hardest tail tasks. V4 handles daily coding; Claude is the safety net for complex multi-file refactors.
V4 coding detailed test (with more case studies) → coding benchmark page
| Dimension | DeepSeek V4-Pro | GPT-5.5 | Claude Opus 4.6 |
|---|---|---|---|
| MMLU-Pro (multi-subject) | 87.5% | ~89% | ~88% |
| MATH-500 (math) | ~88% | ~92% | ~90% |
| GPQA (PhD-level science) | ~72% | ~78% | ~75% |
| Chinese understanding | 94.25% | 92.25% | 91.0% |
| Response speed (TTFT) | 0.6s | 0.8s | 2.4s |
| Stability (72h) | 99.5% | 99.2% | 96.8% |
V4's Chinese score of 94.25% leads GPT-5.5 and Claude 4.6 by 2-3 percentage points — but more importantly, V4 is significantly better at Chinese instruction following, colloquial expression, and localized scenario understanding. This is a hidden advantage for developers in the Chinese market: many edge cases (dialects, local customs, specific industry terminology) are only stable in models trained by Chinese teams.
V4 is in the 1M context first tier, priced at only 1/20 of Gemini.
| Metric | DeepSeek V4 | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|---|
| Max context | 1M | 400K | 1M | 1M |
| MRCR 1M (1M retrieval) | 83.5% | 69.8% | N/A | N/A |
| Output price (per MTok) | $0.87 | $10 | $75 | $15-30 |
V4 and Gemini 3.1 are in the same 1M-context first tier, but V4-Pro output is just $0.87/MTok — about 1/12 of Gemini 3.1 and 1/86 of Claude Opus 4.7. Full 1M context tests → long-context page.
V4 takes the extreme price-performance route. Claude is ~17x the price of V4.
| Model | Input | Output | vs V4-Pro output |
|---|---|---|---|
| DeepSeek V4-Flash | $0.14 / MTok | $0.28 / MTok | 0.32x |
| DeepSeek V4-Pro | $0.435 / MTok | $0.87 / MTok | 1.0x |
| GPT-5.5 Standard | $1.25 / MTok | $10.00 / MTok | 11.5x |
| Claude Opus 4.6 | $15.00 / MTok | $75.00 / MTok | 86.2x |
API price isn't the only cost. Claude and GPT both rely on cloud, and sensitive data leaving the country is a compliance issue. V4 is MIT open source; local deployment has zero license cost, ideal for finance, healthcare, and government-enterprise scenarios — where the hidden compliance cost usually exceeds the API price difference.
These three complement rather than replace each other.
V4 is most cost-effective, $0.28-$0.87/MTok, open-source coding leader.
Pick V4GPT-5.5 leads MMLU-Pro / GPQA / MATH benchmarks comprehensively.
Pick GPT-5.5V4 and Gemini are in the same tier; V4 is 1/12-1/20 the price. Claude Opus 4.7 also supports 1M but ~17x V4's price.
Pick V4Claude completed 38/38 in a 38-task test; V4 completed 29/38. Claude is more reliable for complex agent tasks.
Pick ClaudeV4 Chinese score 94.25% is first, with strong local scenario understanding; native domestic chip adaptation.
Pick V4GPT-5.5 is the only one supporting native audio + video. V4 is text + image only.
Pick GPT-5.5V4 is MIT open source + local deployable, domestic chip adapted. Claude/GPT require cloud + cross-border compliance.
Pick V4Use V4 for daily work (save money), Claude for the hardest tail tasks (ensure stability). One agent routing two models.
V4 + Claude comboDepends on the task. V4-Pro leads GPT-5.5 on coding (LiveCodeBench 93.5% / Codeforces 3206) and Chinese (94.25%); GPT-5.5 leads on math reasoning (MATH-500 ~92%) and multimodal (native audio/video). They're not simple replacements.
V4 in SWE-bench Verified is just 0.2% behind Claude 4.6 (80.6% vs 80.8%); averages are nearly tied. But a 38-task test showed Claude completed 38/38 (100%) vs V4 29/38 (76%). V4 handles daily work; Claude for complex multi-file agent tasks.
V4 series is MIT licensed, with model weights and technical report both published on Hugging Face. Commercial use, modification, and redistribution are unrestricted. Native support for Ascend, Cambricon domestic chips — this combination gives V4 structural advantages in compliance-sensitive industries (finance, healthcare, government-enterprise).
High concurrency (>500 QPS) + cost-sensitive + daily Q&A → Flash ($0.28/MTok output). Agent coding, long-chain reasoning, complex multi-file tasks → Pro ($0.87/MTok but stronger). The two can be mixed; route by task difficulty.
GPT-5.5 is the 2026 iteration aimed at "professional work scenarios". Better long-context coherence, with hallucination rates reduced by 52.5% in medical/legal/financial domains. Slightly slower than GPT-5 but more stable across multi-turn conversations.