DeepSeek V4 vs Frontier Models: GPT-5.5, Claude Opus 4.7, and Gemini 3.1 (2026)
DeepSeek V4 arrived as a preview in spring 2026 with a deliberate claim attached. DeepSeek's own technical report positions V4-Pro-Max as trailing the state-of-the-art frontier by roughly 3 to 6 months while standing as the best open-weights model available. That framing is unusually candid for a vendor, and it turns out to be a fair starting point. This comparison measures DeepSeek V4-Pro against the three closed frontier flagships of 2026, GPT-5.5, Claude Opus 4.7, and Gemini 3.1, using independent leaderboards wherever they exist and labeling every vendor-only figure as vendor-reported. The headline is straightforward: V4 trades the last few points of frontier capability for open MIT weights and pricing that runs many times cheaper. New to the model? Start with our DeepSeek V4 architecture breakdown for context.
Quick Verdict: Best Open-Weights Value, Not the Outright Leader
The verdict is not a single winner. DeepSeek V4-Pro is the best open-weights value of 2026: it delivers near-frontier coding and long-context performance at a fraction of the price, while trailing the closed flagships on the hardest reasoning and agentic tasks. If you need the absolute top of the leaderboard, the frontier trio still holds it. If you need most of that capability with open weights and a far smaller bill, V4 is the strongest case on the table.
- Price: $1.74 / $3.48 per 1M vs $5 / $25 to $30 frontier (vendor)
- Open MIT weights vs closed APIs (vendor)
- SWE-bench Pro: 55.4 edges Gemini 3.1 at 54.2 (independent, LLM Stats)
- SWE-bench Verified: ties Gemini 3.1 at 80.6 (vendor)
- LiveCodeBench: 93.5 above the 91.7 it cites for Gemini (vendor)
- MMLU-Pro: 89.6 to 91.0 vs V4-Pro 87.5 (independent, LMArena)
- GPQA Diamond: ~94 vs V4-Pro-Max ~90.1 (independent, LLM Stats)
- Humanity's Last Exam: 51 to 55 vs 48 (independent, LLM Stats)
- Arena Elo: 1505 to 1506 vs V4-Pro 1467 (independent, LMArena)
- Terminal-Bench 2.0: GPT-5.5 at 82.7 leads the field (independent)
Every number below is labeled vendor-reported (DeepSeek) or independent (LMArena, LLM Stats, Artificial Analysis). V4 is a preview release and independent runs are still accumulating. See the full table and the note on reading vendor tables.
Benchmarks: Where the Gap Is Narrow and Where It Is Not
The benchmark picture splits cleanly. On reasoning and knowledge breadth, the frontier trio holds a measurable lead. On coding, the gap narrows to a few points and DeepSeek V4 edges ahead in places. Each card below labels its source. Independent scores come from LMArena and LLM Stats; vendor scores come from DeepSeek's own technical report and are marked as such.
Full Comparison Table: Every Number, Sourced
The table below collects every benchmark in one place, with a source column noting whether each row is independent or vendor-reported. Where a value is a vendor figure or not directly comparable across models, it is flagged in the source column rather than presented as settled fact.
| Benchmark | DeepSeek V4-Pro | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 | Source |
|---|---|---|---|---|---|
| MMLU-Pro | 87.5 | 89.6 | 89.9-90.0 | 91.0 | Independent (LMArena) |
| GPQA Diamond | ~90.1 | ~94 | ~94 | ~94 | Independent (LLM Stats) |
| Chatbot Arena Elo | 1467 | 1506 | 1505 | 1505 | Independent (LMArena) |
| Humanity's Last Exam | 48 | 52 | 55 | 51 | Independent (LLM Stats); vendor lists 37.7 for V4 |
| SWE-bench Pro | 55.4 | 58.6 | 64.3 | 54.2 | Independent (LLM Stats) |
| SWE-bench Verified | 80.6 | not listed | 80.8 (Opus 4.6-Max) | 80.6 | Vendor (DeepSeek); cites older Opus 4.6 |
| Terminal-Bench 2.0 | 67.9 | 82.7 | 69.4 | 68.5 | Independent leaderboard |
| LiveCodeBench | 93.5 | not listed | not listed | 91.7 | Vendor (DeepSeek); only Gemini cited |
GPQA row shows V4-Pro-Max at ~90.1; HLE V4 value is 48 (LLM Stats independent) or 37.7 (vendor). SWE-bench Verified and LiveCodeBench rows are vendor-reported and several frontier cells are omitted by DeepSeek, marked "not listed" rather than estimated. Independent figures as of June 2026.
How to Read DeepSeek's Own Comparison Table
DeepSeek's published comparison table looks more favorable than the independent picture, and the reason is straightforward: in several rows it benchmarks V4 against older frontier models rather than the current generation. Knowing where this happens prevents over-reading the vendor numbers.
Several vendor columns benchmark V4 against Opus 4.6 and GPT-5.4, not the current Opus 4.7 and GPT-5.5. The SWE-bench Verified row, for example, cites Opus 4.6-Max at 80.8. Treat those as comparisons to the previous generation, not the latest one.
For some metrics the vendor table simply does not list Opus 4.7 or GPT-5.5. The LiveCodeBench row cites only Gemini (91.7) alongside V4-Pro-Max (93.5). We mark those gaps "not listed" rather than filling them with estimates.
On Humanity's Last Exam, the vendor reports 37.7 for V4 while the independent LLM Stats run shows 48. Different harnesses and reasoning settings produce different numbers. We report both and lean on the independent figure for cross-model comparison.
V4 is a preview. Benchmarks age fast and independent runs are still accumulating. Any single-month snapshot, including this one, should be treated as a point-in-time reading rather than a final verdict.
Pricing and Value: Where DeepSeek V4 Wins Outright
If the benchmark story is "close on coding, behind on the hardest reasoning," the pricing story is not close at all. This is the axis where DeepSeek V4 wins decisively, and it is the reason the open-weights value framing holds. All prices below are vendor-published list rates and are subject to change.
| Model | Input ($/1M) | Output ($/1M) | Weights | Source |
|---|---|---|---|---|
| DeepSeek V4-Pro | $1.74 | $3.48 | Open (MIT) | Vendor (DeepSeek) |
| DeepSeek V4-Pro (launch promo, ended May 31, 2026) | $0.435 | $0.87 | Open (MIT) | Vendor (DeepSeek) |
| GPT-5.5 | $5.00 | $30.00 | Closed API | Vendor (OpenAI) |
| Claude Opus 4.7 | $5.00 | $25.00 | Closed API | Vendor (Anthropic) |
| Gemini 3.1 | see vendor pricing | see vendor pricing | Closed API | Not in source set; consult Google |
Gemini 3.1 API pricing is not in our verified source set and is left as "see vendor pricing" rather than estimated. All other rates vendor-published, verified June 2026.
Read together, the numbers describe a clear trade. You give up the last few points of reasoning and Elo, and you give up the GPT-5.5 lead on autonomous terminal work. In return you get coding within a few points of the frontier, long-context capability, open MIT weights, and a bill that is many times smaller. For high-volume coding and agentic pipelines where cost scales with token throughput, that trade is often decisive. For a full rate breakdown, see the DeepSeek pricing guide.
Who Should Use Which?
The decision turns on two questions: do you need the absolute top of the leaderboard on hard reasoning, and how much does token cost matter at your volume? The framework below maps the common cases.
- You need open MIT weights you can self-host for data residency, customization, or vendor independence
- Your workload is high-volume coding or agentic, where token cost dominates and V4 sits within a few points of the frontier
- You want near-frontier capability at a fraction of the price, roughly 7 to 9 times cheaper on output at list rates
- Your coding tasks map to SWE-bench Pro and LiveCodeBench, where V4 edges Gemini 3.1 and stays close to GPT-5.5
- You need the top of the leaderboard on hard reasoning (GPQA Diamond, Humanity's Last Exam), where the trio leads by several points
- Your work is autonomous terminal agents and you want the leader (GPT-5.5 at 82.7 on Terminal-Bench 2.0)
- You want the strongest agentic coding model outright (Claude Opus 4.7 leads SWE-bench Pro at 64.3)
- You need the broadest knowledge benchmark scores (Gemini 3.1 Pro tops MMLU-Pro at 91.0) and cost is secondary
For most teams the honest answer is "both, by task." Route hard-reasoning and top-tier agentic work to a frontier API, and route the high-volume coding and bulk inference to DeepSeek V4 to control cost. The open MIT weights make that split practical in a way a fully closed stack cannot. For more on V4 in production coding, see our DeepSeek for coding and agentic workflows guide.
Frequently Asked Questions
Not on the hardest tasks. DeepSeek's own technical report positions V4-Pro-Max as trailing the state-of-the-art frontier by roughly 3 to 6 months while being the best open-weights model available. On independent LMArena data, V4-Pro scores 87.5 on MMLU-Pro, behind Gemini 3.1 Pro at 91.0, Opus 4.7 at 89.9 to 90.0, and GPT-5.5 at 89.6. On GPQA Diamond it sits around 90.1 (vendor and LLM Stats) versus roughly 94 for the frontier trio, and on Humanity's Last Exam it scores 48 (LLM Stats independent) versus 51 to 55 for the closed models. It closes most of the gap on coding.
Substantially, on vendor-published rates. DeepSeek V4-Pro lists at $1.74 per million input tokens and $3.48 per million output, with a promotional rate of $0.435 input and $0.87 output through May 31, 2026. GPT-5.5 lists at $5 input and $30 output; Claude Opus 4.7 at $5 input and $25 output. At standard rates that makes V4-Pro output roughly 7 to 9 times cheaper than the frontier closed models, and the promotional output rate is roughly 30 to 35 times cheaper. All figures are vendor-reported and subject to change.
On coding the gaps are narrow and V4 edges ahead in places. On SWE-bench Pro (independent, LLM Stats), V4-Pro scores 55.4, slightly ahead of Gemini 3.1 at 54.2, though behind Opus 4.7 at 64.3 and GPT-5.5 at 58.6. On DeepSeek's own SWE-bench Verified table, V4-Pro-Max ties Gemini 3.1 at 80.6. DeepSeek's vendor LiveCodeBench figure of 93.5 is above the 91.7 it cites for Gemini. The decisive advantage, though, is price and open MIT weights rather than raw benchmark wins.
Because the vendor table compares against older models. DeepSeek's published GPQA Diamond row shows V4-Pro-Max at 90.1 against Gemini at 94.3, but several columns are benchmarked against Opus 4.6 and GPT-5.4 rather than the current Opus 4.7 and GPT-5.5, and some 4.7 and 5.5 cells are omitted entirely. We use independent trackers such as LMArena and LLM Stats for the newest-generation numbers and flag every vendor-only figure as vendor-reported. V4 is also a preview release, so independent runs are still accumulating.