Inkling vs DeepSeek V4 Pro: Which Open-Weight Model Wins for Coding, Factuality, and Cost?
Inkling vs DeepSeek is one of the most interesting open-weight matchups of 2026: two fully open models, two permissive licenses, and two very different strengths. Inkling, the debut model from Mira Murati’s Thinking Machines Lab, is a 975B-parameter multimodal MoE built for customization and efficiency. DeepSeek V4 Pro is the latest from the Chinese lab that helped popularize the open-weight coding model, and it arrives with a well-earned reputation for software engineering. This head-to-head compares the two on benchmarks, coding, factuality, licensing, VRAM and cost, so you can decide which one belongs in your stack.
A note for builders: there are no audited head-to-head benchmarks here, so this compares models and access, not scores. OrcaRouter routes API-available models behind a single OpenAI-compatible endpoint, so you can trial and compare Inkling and DeepSeek V4 Pro without wiring up multiple SDKs.
TL;DR verdict: Pick DeepSeek V4 Pro if raw agentic coding is your top priority — it edges Inkling on SWE-bench Verified. Pick Inkling if you care about robustness, factuality, token efficiency, audio/image input, or a 1M-token context window, where it leads by wide margins. Both are open weights and royalty-free to self-host.
Key takeaways
Both are open weights. Inkling ships under Apache 2.0; DeepSeek V4 Pro ships under the MIT license. Both allow commercial use and royalty-free self-hosting.
DeepSeek wins coding by a hair: 80.6% vs 77.6% on SWE-bench Verified (MarkTechPost).
Inkling wins robustness decisively: 78.0% vs 36.0% on the adversarial FORTRESS benchmark (MarkTechPost).
Factuality gap is large: Artificial Analysis reports Inkling as net-positive on AA-Omniscience, while DeepSeek V4 Pro/Flash post very high hallucination rates.
Inkling is more efficient: ~25K vs ~37K output tokens per task (Artificial Analysis) — meaningful for cost at scale.
Modality edge: Inkling accepts text + image + audio and offers up to a 1M-token context; it is the more versatile multimodal model here.
Disclosure: Benchmarks are vendor self-reported at launch (Effort 0.99) and third-party figures are from Artificial Analysis / MarkTechPost / Vellum / BenchLM; none are independently audited, and competitor numbers may differ from those vendors’ own reported figures. Inkling’s own specs are from Thinking Machines’ model card.
Quick-glance comparison
Maker. Inkling: Thinking Machines Lab; DeepSeek V4 Pro: DeepSeek
License. Inkling: Apache 2.0; DeepSeek V4 Pro: MIT
Open weights. Inkling: Yes; DeepSeek V4 Pro: Yes
Parameters. Inkling: 975B total / 41B active (MoE); DeepSeek V4 Pro: —
Context window. Inkling: Up to 1M tokens (256K hosted); DeepSeek V4 Pro: —
Inputs. Inkling: Text + image + audio; DeepSeek V4 Pro: — (text; not in our data)
Output. Inkling: Text; DeepSeek V4 Pro: Text
Self-host / fine-tune. Inkling: Yes / Tinker platform; DeepSeek V4 Pro: Yes
Hosted price. Inkling: ~$1.87 in / ~$4.68 out per 1M; DeepSeek V4 Pro: — (not in our data)
Empty cells marked “—” mean we do not have an audited figure for DeepSeek V4 Pro in our source data and are not guessing.
Winner by category
Reasoning / Knowledge (HLE). Winner: DeepSeek V4 Pro; Notes: 35.9% vs 29.7% (no tools)
Math (AIME 2026). Winner: Roughly tied; Notes: Inkling 97.1% vs 96.7%
Coding (SWE-bench Verified). Winner: DeepSeek V4 Pro; Notes: 80.6% vs 77.6%
Agentic (Terminal Bench 2.1). Winner: Roughly tied; Notes: 64.0 vs 63.8
Safety / Robustness (FORTRESS). Winner: Inkling; Notes: 78.0% vs 36.0%
Factuality (AA-Omniscience). Winner: Inkling; Notes: Net-positive vs high hallucination
Multimodal / Audio. Winner: Inkling; Notes: Image + audio input; DeepSeek not in our data
Efficiency (tokens/task). Winner: Inkling; Notes: ~25K vs ~37K
Cost / TCO. Winner: Tie (both royalty-free self-host); Notes: Depends on efficiency + hosting
Head-to-head benchmarks
The table below uses one consistent set of head-to-head numbers from MarkTechPost. Bold marks the leader in each row.
HLE (no tools). Inkling: 29.7%; DeepSeek V4 Pro: 35.9%
AIME 2026. Inkling: 97.1%; DeepSeek V4 Pro: 96.7%
SWE-bench Verified. Inkling: 77.6%; DeepSeek V4 Pro: 80.6%
Terminal Bench 2.1. Inkling: 63.8; DeepSeek V4 Pro: 64.0
FORTRESS (adversarial). Inkling: 78.0%; DeepSeek V4 Pro: 36.0%
A few “quiet wins” from Artificial Analysis sit outside the MarkTechPost table but matter just as much for real deployments:
Token efficiency (lower is better): Inkling ~25K vs DeepSeek V4 Pro ~37K output tokens per task.
AA-Omniscience factuality: Inkling is net-positive; DeepSeek V4 Pro/Flash are negative, with reported hallucination rates around 94%/96%.
τ³-Banking: Inkling 24 vs DeepSeek V4 Flash 23.
GDPval-AA v2 Elo (agentic): Inkling 1238 vs DeepSeek V4 Flash 1189.
Editor note — add visual: A grouped bar chart of the five MarkTechPost rows would make the split verdict (DeepSeek on HLE/SWE-bench, Inkling on FORTRESS) instantly legible.
Where DeepSeek V4 Pro wins
DeepSeek’s reputation as a coding model holds up here. It leads Inkling on SWE-bench Verified (80.6% vs 77.6%), the most watched real-world software-engineering benchmark, and edges it on HLE (35.9% vs 29.7%) and Terminal Bench 2.1 (64.0 vs 63.8). If your primary workload is autonomous bug-fixing, pull-request generation, or agentic terminal work, DeepSeek V4 Pro is the stronger raw coder in this pairing — and its MIT license makes it trivial to embed in commercial products.
That coding lead is genuine and worth respecting. For teams whose success metric is “how many issues can the agent close,” DeepSeek’s few extra points on SWE-bench Verified can translate into measurable throughput.
Where Inkling wins
Inkling’s advantages are broader and, in several cases, dramatic:
Robustness: On the adversarial FORTRESS benchmark, Inkling scores 78.0% to DeepSeek’s 36.0% — a gap that suggests Inkling is far more resistant to jailbreaks and adversarial prompts.
Factuality: Artificial Analysis puts Inkling net-positive on AA-Omniscience, while DeepSeek V4 Pro/Flash post very high hallucination rates. For RAG, research, and any factual workload, this is a decisive edge.
Efficiency: At ~25K output tokens per task versus ~37K, Inkling gets to the answer with roughly a third less generation — which lowers latency and per-task cost.
Multimodality: Inkling accepts text, images, and audio and evaluates strongly on VoiceBench (91.4%) and MMMU Pro (73.3%). DeepSeek V4 Pro is not in our data as a multimodal model.
Context: Inkling’s weights support up to a 1M-token context (256K on hosted APIs), useful for whole-repo or long-document reasoning.
Agentic quality: Higher GDPval Elo (1238 vs 1189 for V4 Flash) and a marginally better τ³-Banking score.
In short, DeepSeek wins the narrow coding sprint; Inkling wins almost everywhere reliability, honesty, and versatility matter.
Pricing and cost / TCO
Both models are open weights and royalty-free to self-host, so your true cost is infrastructure plus (optionally) hosted inference and fine-tuning.
Inkling hosted (Artificial Analysis): ~$1.87 / 1M input and ~$4.68 / 1M output tokens at 64K context (cache ~$0.374/1M); roughly $3.74/$9.36 at 256K. Fine-tuning runs through the Tinker platform (64K/256K options, 50% limited-time launch discount). A free Playground is available.
DeepSeek V4 Pro: we do not have audited hosted pricing in our source data, so we won’t quote a number. As an MIT-licensed open model it is royalty-free to self-host, and DeepSeek historically prices hosted access aggressively.
The subtler TCO factor is token efficiency. Because Inkling uses ~25K tokens per task versus ~37K for DeepSeek V4 Pro, a workload billed per output token can be meaningfully cheaper on Inkling even at similar per-token rates — and it also finishes faster.
Licensing and deployment
Licensing. Inkling is Apache 2.0; DeepSeek V4 Pro is MIT. Both are permissive, commercial-friendly, and impose no royalties for self-hosting. Apache 2.0 adds an explicit patent grant; MIT is shorter and simpler. For most companies either is fully usable in production — this is a rare comparison where licensing is not a differentiator.
How to run Inkling. Weights are on Hugging Face with both a BF16 and an NVFP4 checkpoint. VRAM tiers:
BF16: ~2TB (8×B300 or 16×H200).
NVFP4: ~600GB (4×B300 or 8×H200) — the practical production tier on Blackwell.
Constrained setups: an Unsloth 1-bit GGUF exists for experimentation.
Supported runtimes include SGLang, vLLM, TokenSpeed, Unsloth, and Hugging Face transformers, and hosted providers include Together AI, Fireworks, Modal, Databricks, and Baseten. A minimal vLLM quickstart:
vllm serve thinkingmachines/Inkling --tensor-parallel-size 8
DeepSeek V4 Pro is likewise available as open weights for self-hosting under MIT; consult DeepSeek’s own model card for its exact checkpoint formats and VRAM requirements, which are not captured in our source data.
Which should you choose?
Choose DeepSeek V4 Pro if coding throughput is your single most important metric, you want the strongest raw SWE-bench Verified score in this pair, and you don’t need multimodal input or a 1M-token context.
Choose Inkling if you need robustness against adversarial prompts, low hallucination rates, token/cost efficiency, audio or image input, a huge context window, or a first-class fine-tuning path via Tinker.
Run both if you can: route coding-heavy agent tasks to DeepSeek and factual, multimodal, or long-context work to Inkling. Because both are royalty-free open weights, a two-model deployment carries no licensing penalty.
For the full picture on Inkling’s architecture and independent scores, see our Inkling AI model review. You can also compare it against other open-weight rivals in our Inkling vs Kimi K2.6 and Inkling vs GLM 5.2 head-to-heads, or start with the basics in what is Inkling AI.
FAQ
Is Inkling better than DeepSeek V4 Pro? It depends on the task. DeepSeek V4 Pro leads on SWE-bench Verified coding (80.6% vs 77.6%) and HLE, while Inkling leads decisively on robustness (FORTRESS 78.0% vs 36.0%), factuality, token efficiency, and multimodal/long-context capability.
Which is better for coding? DeepSeek V4 Pro, narrowly, on the SWE-bench Verified and HLE benchmarks in our MarkTechPost data. Inkling remains a strong coder (77.6% SWE-bench Verified) and is close on Terminal Bench 2.1 (63.8 vs 64.0), so the gap is small.
Which is cheaper? Both are royalty-free to self-host. Inkling’s hosted price is about $1.87/$4.68 per 1M input/output tokens, and its lower token usage per task (~25K vs ~37K) can make it cheaper in practice. We don’t have audited hosted pricing for DeepSeek V4 Pro.
Is DeepSeek V4 Pro open source? It is released under the permissive MIT license with open weights, which allows commercial use and self-hosting. Note that “open weights” is not identical to fully open-source (training data and full pipeline are typically not released), the same nuance that applies to Inkling.
Can I self-host or fine-tune either model? Yes. Both ship open weights for royalty-free self-hosting. Inkling additionally offers a managed fine-tuning path via the Tinker platform (64K/256K context, with a limited-time launch discount); DeepSeek weights can be fine-tuned with standard open tooling.
Which one hallucinates less? Inkling. Artificial Analysis reports Inkling as net-positive on AA-Omniscience factuality, while DeepSeek V4 Pro/Flash show very high hallucination rates (around 94%/96%), making Inkling the safer choice for factual and retrieval-heavy workloads.
Conclusion
DeepSeek V4 Pro is the better pure coder in this matchup and its MIT license makes it easy to ship, but its factuality and robustness scores are real liabilities. Inkling trades a few points of SWE-bench coding for large wins in reliability, honesty, efficiency, and multimodal reach — plus a 1M-token context. For most teams, Inkling is the safer general-purpose open model; for coding-first agent fleets, DeepSeek V4 Pro earns its place. Both being royalty-free open weights, the smartest answer is often to deploy them side by side.
