DeepSeek V4 vs Qwen, GPT, Claude, Kimi and MiniMax: Which Model Wins in 2026
DeepSeek V4 is now available — and it changes how you should evaluate every alternative. The lab shipped two tiers simultaneously: V4-Pro (1.6T parameters, 49B active) and V4-Flash (284B parameters, 13B active), both MIT-licensed and already on the API. With V4-Pro matching GPT-5.4 on MMLU-Pro and leading LiveCodeBench at 93.5, the question is no longer which DeepSeek V4 alternatives to use while you wait — it's how V4 compares to Qwen3.5, Kimi K2.5, MiniMax M2.7, GPT-5.4, and Claude Opus 4.6, and when each one wins.
DeepSeek V4 at a Glance: Pro and Flash
DeepSeek shipped V4 as a two-tier release. V4-Pro is the flagship: a 1.6-trillion-parameter Mixture-of-Experts model that activates 49 billion parameters per token. It supports a 1 million token context window, requires only 27% of single-token inference FLOPs compared to V3.2 at equivalent context lengths, and 10% of KV cache at the 1M-token ceiling. V4-Flash is the efficiency variant: 284 billion total parameters, 13 billion active, near-Pro benchmark quality, and priced at a fraction of the flagship.
| Spec | V4-Pro | V4-Flash |
|---|---|---|
| Total parameters | 1.6T | 284B |
| Active parameters | 49B | 13B |
| Context window | 1M tokens | 1M tokens |
| License | MIT | MIT |
| Input price | $1.74/M tokens | $0.14/M tokens |
| Output price | $3.48/M tokens | $0.28/M tokens |
Both models are available via the DeepSeek API — switch by changing the model name to deepseek-v4-pro or deepseek-v4-flash. Both are MIT-licensed and on Hugging Face for self-hosted deployments.
V4 vs the Frontier: GPT-5.4 and Claude Opus 4.6
The most important comparison is between V4-Pro and the closed-source frontier. This is where the value case for DeepSeek becomes concrete.
GPT-5.4 sits at the top of most multi-domain leaderboards. It excels at instruction following, tool use, and structured output generation. MMLU-Pro: 87.5 — an exact tie with V4-Pro. GPT-5.4 edges ahead on abstract reasoning and multi-step planning, but its input pricing runs $15–20 per million tokens, roughly 9–11× the cost of V4-Pro. For high-throughput applications, that gap is decisive.
Claude Opus 4.6 is Anthropic's coding and long-context specialist. MMLU-Pro: 89.1 — a modest lead over V4-Pro. SWE-bench Verified: ~80.8%, compared to V4-Pro's 80.6%. The two models are statistically indistinguishable on coding benchmarks. Claude Opus 4.6 adds 200K context and the strongest available guardrails, but lists at $15 per million input tokens — more than 8× V4-Pro's $1.74. For teams where compliance and auditability justify the premium, Claude remains the default. For teams optimizing for throughput and cost, V4-Pro offers near-identical coding performance at a fraction of the price.
V4-Pro scores 80.6% on SWE-bench Verified — within 0.2 points of Claude Opus 4.6 — and costs $3.48 per million output tokens versus Claude's $25. That is a 7× price gap at near-identical coding benchmark performance.
DeepSeek V4 vs Qwen3.5 — Open-Source Head-to-Head
Alibaba's Qwen3.5 is the most credible open-weight alternative to V4-Pro on capability per dollar. Released under Apache 2.0, the 397B MoE flagship supports 201 languages, native vision, and 256K context — making it unusually broad in scope.
Benchmarks and Performance
V4-Pro leads on raw coding: LiveCodeBench 93.5 versus Qwen3.5-397B's estimated low-80s range. On SWE-bench Verified, V4-Pro reaches 80.6%; Qwen3.5's top efficient variant (3.6-35B-A3B) hits 73.4% with only 3B active parameters — a remarkable efficiency result, but still a 7-point gap against V4-Pro. Qwen3.5's multimodal breadth makes it the right call for vision-heavy workloads where V4 currently has no advantage.
Pricing and Deployment
Qwen3.5-9B via API costs as little as $0.10 per million input tokens — cheaper than V4-Flash at $0.14/M. For batch inference on simpler workloads, Qwen3.5-9B wins on cost. V4-Flash is the better choice when you need stronger coding and reasoning capability without jumping to Pro pricing.
API Compatibility
Qwen3.5 supports OpenAI-compatible chat completions and function calling. Migrating from DeepSeek or GPT with minimal code changes is straightforward on either stack.
DeepSeek V4 vs Kimi K2.5 — Coding at Scale
Kimi K2.5, developed by Moonshot AI, is a 1T parameter MoE model with 32B active parameters — architecturally similar to the V4-Flash tier in activation density, but built specifically for coding and agentic tasks.
SWE-Bench and Agentic Results
Kimi K2.5 achieves 65.8% pass@1 on SWE-bench Verified with bash/editor tools, and 47.3% on SWE-bench Multilingual. V4-Pro's 80.6% SWE-bench Verified is a clear 15-point lead. For pure coding agent pipelines, V4-Pro is the stronger choice. K2.5 remains competitive for multilingual software tasks where its dedicated training shows.
API, Tooling, and Context Window
Both Kimi and V4 expose OpenAI and Anthropic-compatible APIs. Kimi K2.5 is priced at $0.60/M input — more expensive than V4-Flash ($0.14/M) and less capable on coding benchmarks. Kimi's multimodal audio capabilities differentiate it for speech and voice-integrated applications where V4 has no current offering.
DeepSeek V4 vs MiniMax M2.7 — The Architecture Wild Card
MiniMax M2.7 is architecturally distinct from every other model in this comparison. Its hybrid attention and mixture-of-experts design makes it the most efficient model for long-context inference — and for self-hosting on constrained hardware.
SWE-Bench and Deployment Efficiency
MiniMax M2.7 reaches approximately 80.2% on SWE-bench Verified — statistically comparable to both V4-Pro (80.6%) and Claude Opus 4.6 (~80.8%). The three models form a cluster at the frontier of open-accessible coding performance. MiniMax M2.7 self-hosts on 4× H100 FP8, making it viable for on-premises enterprise deployments where sending code to an external API is not acceptable.
Pricing and API
MiniMax-M2 API pricing is $0.30 per million input tokens and $1.20 per million output tokens — between V4-Flash and V4-Pro, and with near-Pro coding benchmark parity. For teams that need API access without self-hosting, V4-Flash at $0.14/M input offers comparable capability at less than half the price.
Full Benchmark and Pricing Comparison
The following table reflects the current state of the field as of April 2026, with V4's confirmed public benchmarks replacing all earlier speculative figures.
| Model | Params (Active) | Context | SWE-bench Verified | MMLU-Pro | LiveCodeBench | Input price/M | License |
|---|---|---|---|---|---|---|---|
| DeepSeek V4-Pro | 1.6T / 49B | 1M | 80.6% | 87.5 | 93.5 | $1.74 | MIT |
| DeepSeek V4-Flash | 284B / 13B | 1M | ~75% (est.) | ~82 (est.) | ~85 (est.) | $0.14 | MIT |
| Claude Opus 4.6 | Closed | 200K | ~80.8% | 89.1 | 88.8 | $15.00 | Proprietary |
| GPT-5.4 | Closed | 128K | ~80% | 87.5 | — | $15–20 | Proprietary |
| Qwen3.5-397B | 397B / 22B | 256K | ~73% | ~82 | — | $0.10–0.50 | Apache 2.0 |
| Kimi K2.5 | 1T / 32B | 128K | 65.8% | — | — | $0.60 | MIT |
| MiniMax M2.7 | 230B / 10B | 256K | ~80.2% | — | — | $0.30 | Open weights |
Codeforces rating for V4-Pro: 3206 — a competitive programming benchmark that reflects sustained algorithmic reasoning under contest conditions, where V4-Pro leads all currently published models.
Which Model Should You Use?
The decision depends on your use case, budget, and whether you need open weights for self-hosting.
- Best raw coding performance at reasonable cost: DeepSeek V4-Pro. 80.6% SWE-bench Verified, 93.5 LiveCodeBench, Codeforces 3206 — at $1.74/M input, it undercuts Claude and GPT-5.4 by 8–11× on price for near-identical output quality.
- Best budget API for coding: DeepSeek V4-Flash at $0.14/M input. For high-volume code generation, PR review automation, and CI pipelines, Flash delivers strong benchmark performance at a cost that makes even GPT-4o look expensive.
- Best open-weight for self-hosting: MiniMax M2.7 on 4× H100 FP8, or Qwen3.5-9B on a single consumer GPU for lighter workloads. Both are viable for teams that cannot send code to external APIs.
- Multilingual or multimodal applications: Qwen3.5 — Apache 2.0, 201 languages, vision support, strong tool-calling across LangChain and AutoGen.
- Enterprise compliance and auditability: Claude Opus 4.6 remains the standard for teams with stringent guardrail and audit requirements. The capability gap versus V4-Pro is negligible; the compliance tooling is not.
- Maximum 1M context at low cost: DeepSeek V4-Pro is the only model at this capability level that isn't priced as a premium product. At 1M tokens with MIT licensing and 10% KV cache overhead, it opens use cases — full-codebase analysis, long-session agents — that were previously economically impractical.
The 2026 open-weight field has genuinely closed the gap with the frontier. Developers no longer have to choose between capability and cost — V4 has made that trade-off largely obsolete for coding workloads.
Migrating to DeepSeek V4
If you're currently running on an older DeepSeek version or a competing API, migration is minimal. Change the model name in your API call to deepseek-v4-pro or deepseek-v4-flash. Both models use the same OpenAI-compatible endpoint structure. Previous DeepSeek setup guides cover environment configuration that applies to V4 without modification.
For teams considering self-hosting, both V4-Pro and V4-Flash are available on Hugging Face under MIT license. V4-Pro's 49B active parameters require substantial GPU infrastructure; V4-Flash's 13B active parameters bring self-hosting into reach for well-resourced engineering teams.
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