DeepSeek Alternatives in 2026
8 open-weight and hosted LLMs compared on licensing, context window, and price per token, so you know where DeepSeek V4 still wins and where another model fits your stack better.
What is DeepSeek?
DeepSeek V4, released as a preview on April 24, 2026, ships in two variants: V4 Pro (1.6 trillion total parameters, 49 billion active) and V4 Flash (284 billion total, 13 billion active), both with a 1 million token context window and 384K max output. V4 Pro scores 80.6% on SWE-Bench Verified, tying Gemini 3.1 Pro and coming within a point of Claude Opus 4.6, while leading the open-weight field on LiveCodeBench at 93.5%. Both models replaced DeepSeek's entire prior lineup, V3.2 and the R1 reasoning line, with thinking and non-thinking modes built into the same model.
The headline is pricing. DeepSeek made its 75% promotional discount permanent in May 2026, settling V4 Pro at $0.435/M input and $0.87/M output, roughly 34x cheaper than Claude Opus 4.7 on input and 86x cheaper on output. V4 Flash, at $0.14/M input and $0.28/M output, is among the cheapest frontier-class models available at any capability level. Both are MIT licensed, open-weight on Hugging Face, self-hostable, and expose OpenAI- and Anthropic-compatible API endpoints.
The tradeoffs are real, though. The "Preview" label reflects actual rough edges, a multi-turn reasoning_content error has reportedly broken several popular client integrations on first contact, and the legacy deepseek-chat/deepseek-reasoner aliases retire on July 24, 2026, requiring a migration for anyone with hardcoded model names. DeepSeek also originates from a Chinese lab, which matters for teams with jurisdiction-based compliance requirements. The alternatives below cover the open-weight competitors that have closed the gap with DeepSeek on capability, the Western options for compliance-sensitive teams, and ways to access all of them without committing to one.
Kimi K2.6 (Moonshot AI)
Website: moonshot.ai, or via aggregators like OpenRouter
Best for: The single best-rated open-weight model overall, especially for long-horizon coding and agentic orchestration
Starting price: Open weights (MIT), self-host or via API providers
Best Overall Open-Weight: #1 on the Artificial Analysis Intelligence Index among open models
Kimi K2.6, released April 20, 2026, currently leads the Artificial Analysis Intelligence Index among open-weight models and ranks #4 overall behind only closed frontier models. It also tops SWE-Bench Pro at 58.6%, ahead of both GLM-5.1 and DeepSeek V4 on that specific benchmark, while DeepSeek V4 Pro retains the lead on SWE-Bench Verified and the GDPval-AA agentic leaderboard.
The practical difference is in agentic profile rather than raw capability gap: Kimi K2.6 is specifically rated best for long-horizon coding, visual workflows, and autonomous task orchestration, while DeepSeek V4 Pro is rated best for million-token agent traces and long-context software tasks. Both are MIT licensed and follow the same self-hosting and OpenAI-compatible API patterns, so switching between them is mostly a benchmarking decision rather than an integration one.
Pros
- ✓#1 open-weight model on the Artificial Analysis Intelligence Index as of mid-2026
- ✓Leads SWE-Bench Pro among open models, ahead of DeepSeek V4 and GLM-5.1
- ✓MIT licensed, open weights, self-hostable with the same patterns as DeepSeek
- ✓Specifically strong at visual workflows and long-horizon autonomous tasks
- ✓One of the most current open-weight releases (April 20, 2026)
Cons
- ✗Open-weight leaderboard rankings have shifted multiple times in 2026, re-validate before committing
- ✗Smaller ecosystem and tooling maturity than Qwen or Llama
- ✗Originates from a Chinese lab, same jurisdiction considerations as DeepSeek
- ✗Running at full scale requires the same serious GPU infrastructure as DeepSeek V4 Pro
Pricing
| Option | Price |
|---|---|
| Self-hosted | Free (MIT license), GPU infrastructure cost only |
| Via API providers | Check current rates, typically comparable to DeepSeek V4 Pro |
Qwen3.5 / Qwen3.6 (Alibaba)
Website: qwen.ai, Alibaba Cloud
Best for: Broadest multilingual coverage and Apache 2.0 licensing
Starting price: Open weights (Apache 2.0), self-host or via Alibaba Cloud/aggregators
Multilingual Leader: 200+ languages, Apache 2.0 instead of MIT
Qwen3.5 supports more than 200 languages and dialects, the broadest multilingual coverage of any model in this comparison, with Qwen3.7 Max extending agentic capabilities further. The licensing distinction matters for some teams: where DeepSeek V4 ships under MIT, most Qwen3 variants use Apache 2.0, which includes an explicit patent grant that some legal departments specifically require.
Smaller variants matter here too. Qwen3.6-27B and Qwen3.6-35B-A3B are rated among the best practical choices for local and private coding assistants, a far more deployable footprint than DeepSeek V4 Pro's 1.6 trillion parameters, while the largest Qwen3.5 397B variant still competes at the frontier-capability tier for teams with the infrastructure to run it.
Pros
- ✓200+ languages and dialects, the broadest multilingual coverage of any model here
- ✓Apache 2.0 licensing across most Qwen3 variants, including an explicit patent grant
- ✓Qwen3.6-27B/35B run practically as local, private coding assistants
- ✓One of the most mature fine-tuning ecosystems, alongside Mistral
- ✓Qwen3.7 Max extends agentic capabilities for more complex workflows
Cons
- ✗Qwen3.5 397B still requires serious GPU infrastructure for frontier-tier capability
- ✗Trails Kimi K2.6 and DeepSeek V4 Pro on SWE-Bench Pro specifically
- ✗Same Chinese-lab jurisdiction considerations as DeepSeek
- ✗Many Qwen3 variants exist, which can complicate choosing the right one for a given task
Pricing
| Option | Price |
|---|---|
| Self-hosted | Free (Apache 2.0), GPU infrastructure cost only |
| Via API providers | Check current rates |
GLM-5.1 (Zhipu AI / Z.ai)
Website: z.ai
Best for: Enterprise agentic engineering with the cleanest possible open license
Starting price: Open weights (MIT), self-host or via API providers
Cleanest License: MIT with no clauses to read, leads SWE-Bench Pro among open models
GLM-5.1 is MIT licensed, the same as DeepSeek V4, and is specifically called out for having "no clauses to read", a meaningful simplification for legal review compared to licenses with attribution or field-of-use restrictions. On capability, GLM-5.1 leads SWE-Bench Pro among open models at 58.4%, just behind Kimi K2.6 and ahead of DeepSeek V4 Pro on that particular benchmark, while its predecessor GLM-5 already led Arena Elo among open models at 1451.
The combination of top-tier coding capability and MIT licensing is what gets GLM-5.1 specifically rated best for enterprise agentic engineering, a use case where both the model's ability to execute multi-step coding tasks and the simplicity of the license matter to procurement and legal teams simultaneously.
Pros
- ✓MIT licensed with minimal legal review overhead, identical licensing simplicity to DeepSeek V4
- ✓Leads SWE-Bench Pro among open models (58.4%), competitive with Kimi K2.6
- ✓Specifically rated best for enterprise agentic engineering use cases
- ✓Predecessor GLM-5 already led Arena Elo among open models (1451)
- ✓Fast iteration cadence (GLM-5 to 5.1 in a short window)
Cons
- ✗Slightly behind Kimi K2.6 on SWE-Bench Pro and the broader AA Index
- ✗Less established Western enterprise adoption than Llama or Mistral
- ✗Same Chinese-lab origin as DeepSeek, jurisdiction considerations apply
- ✗Smaller community and tooling ecosystem than Qwen or Llama
Pricing
| Option | Price |
|---|---|
| Self-hosted | Free (MIT license), GPU infrastructure cost only |
| Via API providers | Check current rates |
Llama 4 (Meta)
Website: llama.meta.com
Best for: US-jurisdiction compliance requirements and the broadest community tooling
Starting price: Open weights (Llama license), self-host or via many hosting providers
Western Alternative: Named compliance option, plus a 10M token context leader
For teams where US-jurisdiction compliance concerns rule out Chinese-lab models entirely, Llama 4 Maverick is specifically named as one of the Western alternatives to DeepSeek V4 and its open-weight peers, alongside Mistral Large 3. Llama 4 Scout adds a distinct capability advantage: up to a 10 million token context window, the longest of any model in this comparison, useful for processing entire codebases or large document sets in a single pass.
Llama's broader advantage isn't raw benchmark scores, where even Llama 3.1 405B trails DeepSeek V4 Pro significantly (43 vs 87 on one aggregate ranking), it's ecosystem maturity. Llama has the broadest community tooling and support of any model family discussed here, including older versions, which matters for teams whose infrastructure and integrations were already built around Llama before DeepSeek V4 existed.
Pros
- ✓Named Western alternative for US-jurisdiction compliance requirements
- ✓Llama 4 Scout's 10M token context window leads every model in this comparison
- ✓Broadest community tooling and ecosystem support, even on older Llama versions
- ✓Wide availability across hosting providers and cloud platforms
- ✓Meta's continued investment signals long-term support
Cons
- ✗Benchmark scores trail DeepSeek V4 Pro, Kimi K2.6, and GLM-5.1 on coding/agentic leaderboards
- ✗Llama's license has historically carried more restrictions than MIT or Apache 2.0, check current terms
- ✗Using the full 10M context window effectively requires substantial infrastructure
- ✗Less cost-optimized than DeepSeek V4 Flash for high-throughput workloads
Pricing
| Option | Price |
|---|---|
| Self-hosted | Free (Llama license), GPU infrastructure cost only |
| Via hosting providers | Widely available, check current rates |
Mistral (Large 3 / Small 4)
Website: mistral.ai
Best for: European deployment, data sovereignty, and strict Apache 2.0 licensing
Starting price: Open weights (Apache 2.0 for Small 4), self-host or via Mistral API
European Alternative: Data sovereignty plus the best quality-to-resource ratio
Mistral is named alongside Llama as the Western alternative for teams that need European deployment or strict Apache 2.0 licensing rather than DeepSeek's MIT-licensed, China-based offering. Mistral Small 4 stands out specifically for the best quality-to-resource ratio among open models, running on a single A100-equivalent GPU, a dramatically lighter footprint than DeepSeek V4 Pro's 1.6 trillion parameters.
Mistral Small 4 is also highlighted as a strong production-agent option with function calling, JSON output, and reasoning mode built in, and Mistral has one of the most mature fine-tuning ecosystems available, alongside Qwen. For teams in regulated European markets where data residency rules out routing requests through Chinese-lab infrastructure, Mistral is the most direct substitute.
Pros
- ✓European-based, addressing data sovereignty requirements DeepSeek can't meet
- ✓Mistral Small 4 runs on a single A100-equivalent GPU, the lightest footprint in this comparison
- ✓Apache 2.0 licensing on smaller variants
- ✓Mature, well-documented fine-tuning ecosystem
- ✓Strong production-agent features: function calling, JSON output, reasoning mode
Cons
- ✗Largest Mistral models trail DeepSeek V4 Pro and Kimi K2.6 on top-end benchmarks
- ✗Smaller overall release cadence than the Chinese labs in 2026
- ✗Less aggressive per-token pricing than DeepSeek V4 Flash for pure cost-driven workloads
- ✗European hosting may carry higher base infrastructure costs
Pricing
| Option | Price |
|---|---|
| Self-hosted (Small 4) | Free (Apache 2.0), runs on single A100-equivalent GPU |
| Via Mistral API | Check current rates |
Gemma 4 (Google)
Website: ai.google.dev (Gemma)
Best for: Lightweight local self-hosting on modest hardware, now Apache 2.0
Starting price: Open weights (Apache 2.0), free to self-host
Lightest Local Option: Apache 2.0 since April 2026, runs without a GPU cluster
Gemma 4 shipped April 2, 2026 and moved to Apache 2.0 licensing, joining DeepSeek V4 (MIT) and Qwen3.6 (Apache 2.0) in the small group of cleanly-licensed frontier-adjacent open models. The standout variant is Gemma 4 26B A4B, specifically recommended for local deployment, a dramatically smaller footprint than even DeepSeek V4 Flash's 284 billion total parameters.
For teams whose priority is running something entirely on a single workstation or modest cloud instance, where data never leaves that machine, Gemma 4 is the more realistic starting point than any DeepSeek V4 variant. The tradeoff is capability: Gemma 4 isn't competing with DeepSeek V4 Pro or Kimi K2.6 on frontier coding benchmarks, it's optimized for a different deployment profile entirely.
Pros
- ✓Apache 2.0 license as of the April 2026 release, clean for commercial use
- ✓Gemma 4 26B A4B runs on far more modest hardware than any DeepSeek V4 variant
- ✓Backed by Google's ongoing model development and documentation
- ✓Strong fit for local, private deployments where data can't leave one machine
- ✓Free to self-host with no API costs at all
Cons
- ✗Capability ceiling is well below DeepSeek V4 Pro, Kimi K2.6, or GLM-5.1 for complex agentic tasks
- ✗Best suited to lighter workloads (chat, summarization) rather than frontier coding benchmarks
- ✗Smaller context window than DeepSeek V4 or Llama 4 Scout
- ✗Still requires GPU hardware for reasonable inference speed, even if less than larger models
Pricing
| Option | Price |
|---|---|
| Self-hosted | Free (Apache 2.0), modest hardware requirements |
Gemini 3.5 Flash (Google)
Website: ai.google.dev, Google AI Studio
Best for: Hosted, near-frontier performance without self-hosting or open-weight license review
Starting price: $1.50/M input, $9/M output
No Self-Hosting Required: Near-Pro agentic quality at roughly 4x frontier speed
For teams that want a DeepSeek-like price-performance story without managing open-weight infrastructure or evaluating a Chinese-lab license, Gemini 3.5 Flash offers near-Pro agentic quality at roughly 4x frontier speed, fully hosted on Google's infrastructure. At $1.50/M input and $9/M output, it's considerably more expensive per token than any DeepSeek V4 variant ($0.435/$0.87 for Pro, $0.14/$0.28 for Flash), but that price difference buys a standard commercial API relationship with no self-hosting, no MIT/Apache license review, and none of DeepSeek V4's preview-label operational issues.
Notably, smaller Flash-tier models punching above their parameter count isn't unique to DeepSeek: Gemini 3 Flash has been observed outperforming Gemini 3 Pro on SWE-bench despite being the smaller, distilled model, a similar dynamic to how DeepSeek V4 Flash competes with much larger closed models.
Pros
- ✓Fully hosted, no self-hosting infrastructure or GPU costs required
- ✓Near-Pro agentic quality at roughly 4x frontier speed
- ✓Standard commercial API terms, no open-weight license review needed
- ✓Backed by Google's infrastructure and reliability track record
- ✓Smaller Flash variants have outperformed their own Pro siblings on some coding benchmarks
Cons
- ✗Significantly more expensive per token than any DeepSeek V4 variant
- ✗No self-hosting option, full dependency on Google's API availability and pricing
- ✗Closed model, can't be fine-tuned or modified like the open-weight alternatives
- ✗Less architectural transparency than DeepSeek's published technical reports
Pricing
| Plan | Price |
|---|---|
| Gemini 3.5 Flash | $1.50/M input, $9/M output |
OpenRouter
Website: openrouter.ai
Best for: Accessing DeepSeek, Kimi, Qwen, GLM, Llama, Mistral, and closed models through one API without separate accounts
Starting price: Pay-per-token, pass-through pricing plus a small markup
Try Everything: One API key for every model on this list, including DeepSeek itself
Rather than committing to a single DeepSeek alternative, OpenRouter provides one API endpoint covering most of the models discussed here, DeepSeek V4 itself, Kimi K2.6, Qwen3.5/3.6, GLM-5.1, Llama 4, Mistral, and Gemini 3.5 Flash, with consistent request formatting across providers. Given that five frontier-class open-weight models reportedly shipped within a 30-day window in 2026, and rankings have shifted multiple times since, switching between them through OpenRouter is a one-line configuration change rather than a new account, billing relationship, and integration for each lab.
For teams running agentic workloads who want to benchmark DeepSeek V4 against Kimi K2.6 or GLM-5.1 on their own actual tasks before committing infrastructure to either, this is the lowest-friction way to run that comparison without prematurely locking in.
Pros
- ✓Single API key and consistent request format across DeepSeek, Kimi, Qwen, GLM, Llama, Mistral, and closed models
- ✓Switching models is a one-line config change, valuable given how fast open-weight rankings shift
- ✓No need to manage separate accounts or billing relationships per lab
- ✓Lets teams benchmark alternatives against DeepSeek V4 on real workloads before committing
- ✓Covers both open-weight and closed-model options in one place
Cons
- ✗Small markup over direct provider pricing
- ✗Adds a dependency layer between the application and the underlying model provider
- ✗Self-hosting cost advantages of open-weight models aren't available through a hosted aggregator
- ✗Rate limits and availability depend on OpenRouter's relationships with underlying providers
Pricing
| Option | Price |
|---|---|
| Pay-per-token | Pass-through pricing plus small markup, varies by model |
Side-by-Side Comparison
| Tool | License | Context Window | Self-Hostable | Approx. Pricing (per M tokens) | Best For |
|---|---|---|---|---|---|
| DeepSeek V4 Pro | MIT | 1M | Yes | $0.435 / $0.87 | Agentic coding, million-token traces |
| DeepSeek V4 Flash | MIT | 1M | Yes | $0.14 / $0.28 | Cheapest frontier-class inference |
| Kimi K2.6 | MIT | Large (check provider) | Yes | Comparable to DeepSeek V4 Pro | Best overall open-weight |
| Qwen3.5 / 3.6 | Apache 2.0 | Large (check variant) | Yes | Check current rates | Multilingual (200+ languages) |
| GLM-5.1 | MIT | Large (check provider) | Yes | Check current rates | Enterprise agentic engineering |
| Llama 4 (Scout/Maverick) | Llama license | Up to 10M (Scout) | Yes | Check provider | US compliance, long context |
| Mistral (Small 4) | Apache 2.0 | Standard | Yes | Check current rates | European deployment, light hosting |
| Gemma 4 (26B A4B) | Apache 2.0 | Standard | Yes | Free (self-host) | Local, lightweight deployment |
| Gemini 3.5 Flash | Closed | Standard | No | $1.50 / $9 | Hosted, no self-hosting needed |
| OpenRouter | N/A (aggregator) | Depends on model | No | Pass-through + markup | Switching between all of the above |
Which Should You Choose?
I want the single best-rated open-weight model right now → Kimi K2.6
Currently #1 on the Artificial Analysis Intelligence Index among open models, with the same MIT license and self-hosting pattern as DeepSeek V4.
My users span many languages and I want a clean Apache 2.0 license → Qwen3.5 / Qwen3.6
200+ languages, an explicit patent grant under Apache 2.0, and smaller variants that run as practical local coding assistants.
I need top coding capability with the simplest possible license review → GLM-5.1
MIT licensed with no clauses to read, and leads SWE-Bench Pro among open models.
My compliance team rules out Chinese-lab models entirely → Llama 4
Named as a Western alternative, with Llama 4 Scout's 10M token context as a distinct capability advantage.
I need European data residency or strict Apache 2.0 → Mistral (Small 4)
European-based, runs on a single A100-equivalent GPU, with mature fine-tuning tooling.
I want to self-host something on a single machine, full stop → Gemma 4
Apache 2.0 as of April 2026, with the 26B A4B variant built for local deployment without a GPU cluster.
I want DeepSeek-level performance without managing any infrastructure → Gemini 3.5 Flash
Fully hosted, near-Pro agentic quality at roughly 4x frontier speed, at standard commercial API terms.
I don't want to commit to one model yet → OpenRouter
One API key for DeepSeek V4 and every alternative on this list, so you can benchmark before building infrastructure around any single choice.
DeepSeek V4 Pro and V4 Flash remain genuinely difficult to beat on pure price-per-token, and the MIT license and self-hosting story are real advantages. But 2026's open-weight field moved fast enough that "best open model" has changed hands several times in a matter of weeks, and Kimi K2.6 and GLM-5.1 both lead DeepSeek V4 on specific benchmarks (SWE-Bench Pro) while matching its licensing simplicity. For teams where DeepSeek's Chinese-lab origin is a hard blocker, Llama 4 and Mistral are the named Western alternatives, with Gemma 4 as the lightest local option. And if hosted simplicity matters more than the absolute lowest price, Gemini 3.5 Flash trades a higher per-token cost for zero infrastructure management. Given how quickly this landscape is shifting, OpenRouter's one-API-key approach to testing several of these against DeepSeek V4 on your own workloads is a reasonable way to avoid betting on a ranking that might not hold in another month.