Grok 4.5 vs DeepSeek V4: Aggressive Price vs Open Weights (2026)

Grok 4.5 and DeepSeek V4 are two cost-efficient challenger large language models that reach the same goal from opposite directions. Grok 4.5 is SpaceXAI's closed flagship, public since July 9, 2026, priced at 2 dollars per million input tokens and 6 dollars per million output tokens, with a 500,000-token context window. DeepSeek V4 is DeepSeek's open-weight Chinese model, shipped under an MIT license on Hugging Face on April 24, 2026, with a hosted API — V4-Flash at 0.14 dollars input and 0.28 dollars output per million tokens, and V4-Pro at 0.435 dollars input and 0.87 dollars output, both with a 1,000,000-token context window. On the one independent evaluator that scores both the same way, Artificial Analysis, Grok 4.5 leads the Intelligence Index 54 to 44 for DeepSeek V4-Pro and carries a charted Coding Agent Index of 76, which DeepSeek V4 does not have. DeepSeek V4 is dramatically cheaper, open-weight and self-hostable, doubles the context window, and is available in the European Union where Grok 4.5 is blocked. Best for measured intelligence and a charted coding score: Grok 4.5. Best for price, open weights, long context, and availability: DeepSeek V4. This is a split verdict, not a single winner.

Quick Verdict

This is a split verdict by use case, not a single overall winner. We ran both models through their hosted APIs to confirm they behave as documented, pulled every price directly from each vendor's own pricing page, and leaned on the one independent lab that scores both the same way, Artificial Analysis, for capability. We have not run weeks of controlled, identical-task benchmarking of the two against each other, so wherever a claim rests on a number we attribute it. The honest summary is that these two are not really fighting for the same buyer. Here is the short version.

• Best for measured intelligence: Grok 4.5. On the Artificial Analysis Intelligence Index — the one composite both models are scored on by the same evaluator, on the same version 4.1 — Grok 4.5 sits at 54, while DeepSeek V4-Pro in its maximum reasoning mode scores 44.
• Best for a verified coding score: Grok 4.5. It carries a charted Artificial Analysis Coding Agent Index of 76, roughly level with GPT-5.5. DeepSeek V4 is not on that independent leaderboard; its 80.6 percent SWE-bench Verified figure is self-reported.
• Best for cost: DeepSeek V4, and it is not close. V4-Flash output at 0.28 dollars per million tokens is roughly 21 times cheaper than Grok 4.5 output at 6 dollars, and even V4-Pro at 0.87 dollars is about 7 times cheaper.
• Best for open weights and self-hosting: DeepSeek V4. The weights ship under an MIT license and run on your own hardware, including Huawei Ascend chips. Grok 4.5 is closed and API-only.
• Best for long context and availability: DeepSeek V4. It doubles Grok 4.5's context window (1,000,000 versus 500,000 tokens), offers a free web tier, and is available in the European Union, where Grok 4.5 is blocked under the EU AI Act.

Bottom line: if you want the higher measured intelligence and the only charted coding score, and you can accept a closed model at a still-modest price, pick Grok 4.5. If you want the lowest per-token cost, downloadable weights you can self-host for sovereignty, a longer context window, or access inside the EU, pick DeepSeek V4. We did not crown a single winner because Grok 4.5 wins the two heaviest capability dimensions while DeepSeek V4 wins almost everything else.

At a Glance

Before the detail, here is the side-by-side that frames everything below. All pricing in this table was fetched directly from each vendor's pricing page in July 2026. Every benchmark figure is attributed to its source, and independent scores are kept separate from self-reported ones.

Overview of Each Model

Grok 4.5

Grok 4.5 is SpaceXAI's flagship reasoning model, made publicly available on July 9, 2026 after an announcement the day before, replacing Grok 4.3 at the top of the lineup. It carries a 500,000-token context window, accepts text and image input and returns text, and exposes function calling, structured outputs, and a three-level reasoning-effort control (low, medium, high, with high as the default). On the independent Artificial Analysis Intelligence Index version 4.1 it scores 54, ranking fourth overall behind Claude Fable 5, GPT-5.5, and Claude Opus 4.8, and it carries a charted Coding Agent Index of 76, roughly level with GPT-5.5. Its defining pitch is price for a frontier-tier model: 2 dollars per million input tokens, 0.50 dollars cached input, and 6 dollars per million output tokens, which SpaceXAI positions as roughly half the price of Claude Opus 4.8 and GPT-5.6 Sol. The model is served from US regions with generous rate limits, and its API is OpenAI-compatible, so most existing SDK code runs after a base-URL and model-name change. The trade-offs are real: it is closed and paid-only, its context window is smaller than most rivals, it has no independently verified SWE-bench Verified score yet because it is too new, and it carries a high measured hallucination rate. It is also blocked in the European Union. For the full breakdown, see our Grok 4.5 review.

DeepSeek V4

DeepSeek V4 is the Chinese open-weight flagship, shipped April 24, 2026 in two sizes: V4-Pro, a 1.6-trillion-parameter mixture-of-experts model with about 49 billion parameters active per token, and V4-Flash, a 284-billion-parameter model with about 13 billion active. Both carry a 1,000,000-token context window with up to 384K tokens of output, and both ship under an MIT license that permits free commercial use, redistribution, and modification of the weights — though the training code and data recipe are not released, which makes this open weights rather than fully open source. On the independent Artificial Analysis Intelligence Index version 4.1, DeepSeek V4-Pro in maximum reasoning mode scores 44, which Artificial Analysis ranks as the number-two open-weight reasoning model behind Kimi K2.6. DeepSeek self-reports 80.6 percent on SWE-bench Verified, but the model is not on the independent SWE-bench leaderboard, so we treat that figure as a vendor claim. The architecture is genuinely novel rather than just bigger: a Hybrid Attention design combining Compressed Sparse Attention and Heavily Compressed Attention makes the million-token context affordable to serve, and three built-in thinking modes — Non-Think, Think High, and Think Max — let you dial cost against quality per request. It is the first major Chinese frontier model with day-one inference on Huawei Ascend hardware, and the hosted API is OpenAI-compatible. The headline, though, is price: V4-Flash output sits at 0.28 dollars per million tokens. Our full DeepSeek V4 review covers the architecture and licensing in more depth.

Pricing Compared

This is where the two models diverge most, and it is the single most important thing to understand about the matchup. We fetched every number below directly from each vendor's pricing page in July 2026. Note that both models are already positioned as cost-efficient, so this is a cheap-versus-cheaper contest, not cheap versus premium.

Run the arithmetic and the gap is wide, though narrower than Grok 4.5's low price versus the top tier might lead you to expect. On output tokens — the number most teams care about, because output dominates real agentic spend — Grok 4.5 at 6 dollars is roughly 7 times the cost of V4-Pro at 0.87 dollars, and about 21 times the cost of V4-Flash at 0.28 dollars. On input tokens, Grok 4.5 at 2 dollars is about 4.6 times V4-Pro and about 14 times V4-Flash. On cached input, DeepSeek is in a different universe entirely: V4-Pro cache reads at 0.003625 dollars per million tokens are more than a hundred times cheaper than Grok 4.5's 0.50 dollars, which makes stable-prompt retrieval and tool loops nearly free on DeepSeek.

Two nuances worth flagging honestly. First, DeepSeek's headline prices are the hosted-API path, and a self-hosted deployment is not free — running V4-Pro yourself in full precision needs enterprise GPU clusters, and even V4-Flash needs INT4 or INT8 quantization to fit on a single high-end consumer card. The open weights remove per-token billing but shift cost into hardware and operations. Second, Grok 4.5's 2-and-6-dollar card is genuinely aggressive for its measured capability tier; the comparison here is not between a cheap model and an expensive one, but between a cheap closed model and a much cheaper open one. For most teams the hosted DeepSeek API is the relevant comparison, and there DeepSeek costs several times less per output token than Grok 4.5.

Benchmarks Compared

Benchmarks across two labs are a minefield, because vendors pick favorable evaluations and report them their own way. We discipline this by leaning on the one independent evaluator that scores both models the same way — Artificial Analysis — and treating vendor-reported figures as attributed claims, not verified facts. We are strict about the difference between independent and self-reported numbers here.

The cleanest signal is the Artificial Analysis Intelligence Index, because it is one evaluator running the same battery on both, on the same version 4.1: Grok 4.5 at 54 versus V4-Pro at 44, a clear ten-point lead for Grok 4.5. On coding, only Grok 4.5 has an independent number — a Coding Agent Index of 76 — while DeepSeek V4 is absent from that leaderboard. That does not mean DeepSeek is weak at code; it self-reports 80.6 percent on SWE-bench Verified, which would be strong for an open model, but that figure comes from DeepSeek's own harness and is not independently charted, so we will not treat it as a head-to-head result.

Two honesty notes cut the other way. First, Grok 4.5 is new enough that it has no independently verified SWE-bench Verified score at all, so on that specific benchmark the two are level at "not charted." Second, Grok 4.5's own measured weakness is factual reliability: Artificial Analysis records a 54 percent hallucination rate on its AA-Omniscience test, which means factual work on Grok 4.5 needs retrieval grounding or human review. Elon Musk has described Grok 4.5 as "Opus-class, much faster," but that is a vendor claim, not a measured result, and the independent index places it fourth overall rather than at the top. The numbers we can verify say Grok 4.5 is the stronger model on the composite Intelligence Index and the only one with a charted coding score, and that DeepSeek V4 competes on almost everything else.

Architecture and What Is Actually Different

It is tempting to treat two frontier-adjacent models as interchangeable black boxes you poke through an API, but the engineering underneath shapes how they behave, what they cost to run, and where they can be deployed. These two could hardly be more different in philosophy.

Grok 4.5 is a closed model, so SpaceXAI discloses behavior rather than internals. What it surfaces is a product built for aggressive price-to-capability: a 500,000-token context, text and image input, function calling and structured outputs, and a three-level reasoning-effort control. It is served from US regions with high rate limits, and the API is OpenAI-compatible, which lowers switching cost. SpaceXAI trained the model with coding-agent tooling in the loop and on current-generation accelerators, and Musk has publicly pitched it as close to the top tier at a fraction of the cost. We can confirm the documented behavior and the price, but not the leaked parameter counts or claims of live platform data at the API level, which are unverified, so we leave them out. The practical shape is a cheap, fast, closed model you consume but cannot inspect or move.

DeepSeek V4 is the opposite — transparent at the architecture level because the weights and a technical report ship publicly. It is a mixture-of-experts model: V4-Pro carries 1.6 trillion total parameters with about 49 billion active per token, and V4-Flash carries 284 billion total with about 13 billion active. The headline innovation is a Hybrid Attention design that combines Compressed Sparse Attention with Heavily Compressed Attention to make a 1,000,000-token context affordable to serve, cutting inference compute and shrinking the key-value cache sharply versus the previous generation. Three reasoning modes — Non-Think, Think High, and Think Max — are baked directly into the model rather than bolted on as a separate API, and DeepSeek ships day-one inference on Huawei Ascend hardware, removing NVIDIA dependency for domestic deployments. This is why DeepSeek V4 can be both frontier-adjacent in quality and several times cheaper on output: the efficiency is engineered in, not just priced in.

The practical upshot is that Grok 4.5 gives you a cheap, polished, closed model you cannot inspect or relocate, while DeepSeek V4 gives you an inspectable, movable model you operate yourself. Neither philosophy is wrong; they serve different risk, cost, and sovereignty profiles.

Total Cost of Ownership

Per-token price is the headline, but the real economics depend on volume, caching, and whether you self-host. Here is how to think about it without overstating the case in either direction.

For the hosted-API path, the gap is large enough to change what is buildable at scale. A pipeline that processes a billion output tokens a month costs about 6,000 dollars on Grok 4.5, roughly 870 dollars on DeepSeek V4-Pro, and about 280 dollars on V4-Flash. Those are meaningful multiples — roughly 7 times and 21 times respectively — and at high volume they decide whether an idea is economically viable. Prompt caching widens the gap further on the input side: Grok 4.5 cache reads at 0.50 dollars per million tokens are cheap by frontier standards, but DeepSeek's cache hits at 0.0028 to 0.003625 dollars are almost free, so any workload with a stable system prompt tilts even harder toward DeepSeek. That said, output dominates most agentic spend, and there the multiple is the 7-to-21-times range above rather than the hundred-fold cache gap.

For the self-hosted path, the calculus flips from per-token billing to capital and operations. DeepSeek's open weights remove the API meter entirely, but you pay in hardware: full-precision V4-Pro requires enterprise GPU clusters, and even V4-Flash needs quantization to fit a single high-end consumer card. For a team with steady, predictable, very high volume and the operational maturity to run model infrastructure, self-hosting V4 can be the cheapest option of all and the only one that guarantees data never leaves your premises. Grok 4.5 offers no self-hosting at all, so its floor is the hosted API price. The honest conclusion is that DeepSeek wins on cost in every scenario; the only questions are by how much and at what operational price.

How We Tested

Honesty about methodology matters more in a cross-lab, cross-country comparison than almost anywhere else. Here is exactly what is hands-on and what is research.

We ran both models through their hosted APIs to confirm they behave as documented — Grok 4.5's reasoning-effort control, structured outputs, and OpenAI-compatible endpoint from its US regions, and DeepSeek V4's three thinking modes and OpenAI-compatible endpoint. Those behavioral observations are first-hand. What we have not done is stand up a self-hosted V4-Pro cluster, or run weeks of controlled, identical-task benchmarking of the two models against each other on a private suite. For that reason, every capability claim that rests on a number is attributed to its source — Artificial Analysis for the independent Intelligence Index and Coding Agent Index, and each vendor for its own self-reported figures. We pulled all pricing by fetching each vendor's pricing page directly in July 2026 rather than trusting secondhand summaries, which matters because DeepSeek's V4-Pro price settled at 0.435 dollars input after an introductory discount became permanent. Where we could not verify a like-for-like number — DeepSeek's coding score, Grok 4.5's SWE-bench Verified score — we said so and left the cell uncommitted rather than invent a head-to-head. That is the standard we hold ourselves to.

Winner by Category

A single overall winner would be dishonest here, because these models are tuned for different buyers. Here is who wins what.

• Best for measured intelligence: Grok 4.5. It sits at 54 on the Artificial Analysis Intelligence Index version 4.1, ahead of DeepSeek V4-Pro at 44 on the same evaluator and version.
• Best for a verified coding score: Grok 4.5. It is the only one of the two with a charted Artificial Analysis Coding Agent Index, at 76. DeepSeek V4's coding strength is self-reported, not independently charted.
• Best for cost: DeepSeek V4. Several times cheaper per output token on the hosted API — roughly 7 times cheaper on V4-Pro and 21 times cheaper on V4-Flash — and more than a hundred times cheaper on cached input.
• Best for open weights and self-hosting: DeepSeek V4. MIT-licensed downloadable weights, with native Huawei Ascend support; Grok 4.5 cannot be self-hosted at all.
• Best for long-context work: DeepSeek V4. Its 1,000,000-token window is double Grok 4.5's 500,000, with up to 384K tokens of output.
• Best for availability and a free tier: DeepSeek V4. It offers a free web tier and is available in the European Union, where Grok 4.5 is blocked under the EU AI Act.
• Best for a US-hosted closed option: Grok 4.5. If avoiding China-hosted infrastructure matters and you cannot self-host, Grok 4.5's US regions are the simpler answer — provided you are not in the EU.

Pros and Cons

When to Pick Each

When to pick Grok 4.5

Pick Grok 4.5 when measured capability matters more than the last multiple of cost savings, and you are outside the EU. If you want the higher score on the one independent index that ranks both, the only charted coding number of the pair, and a closed, US-hosted API that stays cheap for its tier, Grok 4.5 is the stronger model on paper and still costs a fraction of the top frontier tier. Pick it if you prefer a managed, closed endpoint over running your own infrastructure, if OpenAI-compatible SDK reuse and US regions simplify your stack, or if you specifically want to avoid China-hosted inference and cannot self-host. Just budget for grounding: its measured hallucination rate is high, so pair it with retrieval or review for anything factual, and remember it is unavailable in the European Union.

When to pick DeepSeek V4

Pick DeepSeek V4 when cost, control, sovereignty, or context length dominate. If you are running high-volume inference where token spend is the binding constraint, a several-times-cheaper API — and a nearly free cached-input path — changes what is economically viable, and V4-Flash makes bulk workloads routine that Grok 4.5 would make expensive. Pick it if you need to own your weights: the MIT license lets you self-host, fine-tune, and redistribute, and the Huawei Ascend support means you are not locked to one chip vendor. Pick it if you need a 1,000,000-token context, a free tier to prototype on, or availability inside the EU, where Grok 4.5 is blocked. You give up a measurable slice of intelligence and the charted coding score, but you get frontier-adjacent quality at a fraction of the price with far more control.

Final Verdict

This is a split verdict, tilted toward Grok 4.5 on measured capability and toward DeepSeek V4 on cost, openness, context, and availability. On the one independent evaluator that scores both on the same version — Artificial Analysis version 4.1 — Grok 4.5 sits at 54 versus 44 for DeepSeek V4-Pro, and it is the only one of the two with a charted coding score, at 76. It is the stronger model on paper and stays cheap for its tier. DeepSeek V4, in return, costs several times less per output token, ships MIT-licensed open weights you can self-host for sovereignty, doubles the context window, and is available in the EU where Grok 4.5 is blocked — a genuinely strong package for an open model.

We did not crown a single overall winner because the two models are not really competing for the same buyer. If you need the higher measured intelligence, the only charted coding score, or a US-hosted closed endpoint, the answer is Grok 4.5. If you need the lowest cost, downloadable weights, a longer context, or EU access, the answer is DeepSeek V4. Both answers are correct — for different people. Every benchmark number here is drawn from Artificial Analysis or attributed to a vendor's own report; only the pricing is fetch-verified directly from each vendor.

If you are weighing these two against other cost-efficient models, we also compared DeepSeek V4 with GPT-5.5, with Claude Sonnet 5, and against fellow open-weight Chinese models in GLM-5.2 vs DeepSeek V4 and Kimi K2.7 vs DeepSeek V4. For the deep dive on each model on its own, see our full Grok 4.5 review and DeepSeek V4 review.

Frequently Asked Questions

Is Grok 4.5 better than DeepSeek V4?

On measured capability, yes. Grok 4.5 leads the independent Artificial Analysis Intelligence Index version 4.1 at 54 versus 44 for DeepSeek V4-Pro, and it is the only one of the two with a charted Coding Agent Index, at 76. But DeepSeek V4 is several times cheaper per output token, ships open weights you can self-host, doubles the context window, and is available in the EU where Grok 4.5 is blocked. The better choice depends on whether you are optimizing for measured intelligence or for cost, control, and availability.

How much cheaper is DeepSeek V4 than Grok 4.5?

Substantially. On output tokens, DeepSeek V4-Flash at 0.28 dollars per million is roughly 21 times cheaper than Grok 4.5 at 6 dollars, and V4-Pro at 0.87 dollars is about 7 times cheaper. On input tokens, V4-Flash at 0.14 dollars is about 14 times cheaper than Grok 4.5 at 2 dollars. On cached input, DeepSeek is more than a hundred times cheaper. All prices were fetched directly from each vendor's pricing page in July 2026.

Is DeepSeek V4 open source?

It is open weights, not fully open source. DeepSeek V4 ships its model weights under an MIT license on Hugging Face, allowing free commercial use, redistribution, and modification. However, the training code and data recipe are not released, so the community cannot fully reproduce the training run. You can self-host and fine-tune the model, but you cannot rebuild it from scratch. Grok 4.5, by contrast, is fully closed and API-only.

Can I self-host Grok 4.5 or DeepSeek V4?

You can self-host DeepSeek V4 because its weights are MIT-licensed and downloadable, including native support for Huawei Ascend chips. You cannot self-host Grok 4.5 — it is a closed model available only through SpaceXAI's API. Self-hosting V4 requires serious hardware: full-precision V4-Pro needs enterprise GPU clusters, and V4-Flash needs quantization to fit a single high-end consumer card.

What is the context window for each model?

Grok 4.5 ships a 500,000-token context window. DeepSeek V4 provides 1,000,000 tokens of context on both V4-Pro and V4-Flash, with up to 384K tokens of output. DeepSeek V4's context window is double Grok 4.5's, which matters for long-document analysis, large codebases, and extended agentic runs.

Which model is better for coding?

On the independent evidence, Grok 4.5 has the edge, because it carries a charted Artificial Analysis Coding Agent Index of 76 and DeepSeek V4 is not on that leaderboard. DeepSeek self-reports 80.6 percent on SWE-bench Verified, which is strong for an open model, but that figure is not independently charted, so we treat it as a vendor claim. For high-volume coding where cost dominates, DeepSeek V4 is far cheaper; for the best independently verified coding signal of the two, Grok 4.5 is the safer pick.

Is DeepSeek V4 safe to use for a Western company?

It depends on your data-residency rules. DeepSeek's hosted API runs in China, which keeps many regulated buyers — US Federal, EU healthcare — from adopting it without a Western reseller. The MIT-licensed open weights let you sidestep this by self-hosting the model on your own infrastructure anywhere in the world. If compliance is the concern and you cannot self-host, Grok 4.5's US hosting is the simpler default, provided you are not in the European Union, where Grok 4.5 is blocked.

How do the two models score on independent benchmarks?

The cleanest independent signal is the Artificial Analysis Intelligence Index version 4.1, which scores both with the same battery: Grok 4.5 sits at 54, ranked fourth overall, while DeepSeek V4-Pro in maximum reasoning mode scores 44, the number-two open-weight reasoning model. Grok 4.5 also has a charted Coding Agent Index of 76, which DeepSeek lacks. Neither model has an independently verified SWE-bench Verified score as of July 2026 — Grok 4.5 is too new, and DeepSeek's 80.6 percent is self-reported.

What are the different DeepSeek V4 tiers?

DeepSeek V4 ships in two sizes. V4-Pro is a 1.6-trillion-parameter mixture-of-experts model with about 49 billion parameters active per token, priced at 0.435 dollars input and 0.87 dollars output per million tokens. V4-Flash is a 284-billion-parameter model with about 13 billion active, priced at 0.14 dollars input and 0.28 dollars output. Both carry a 1,000,000-token context window, and both support three reasoning modes — Non-Think, Think High, and Think Max.

Why is Grok 4.5 blocked in the European Union?

Grok 4.5 is not offered in the European Union because of the EU AI Act's provisions for models designated as carrying systemic risk. For teams based in the EU, that makes DeepSeek V4 the practical choice of the two — either through its hosted API or, for full compliance and data residency, by self-hosting the MIT-licensed open weights on infrastructure inside the EU.

Does Grok 4.5 have a cheaper mode or a free tier?

Grok 4.5 is paid-only through SpaceXAI's API and has no free API tier. Its cost levers are prompt caching, which drops repeated input to 0.50 dollars per million tokens on cache reads, and its already-low 2-dollar input and 6-dollar output rates. DeepSeek V4, by contrast, offers a free web chat tier and a very low-cost API, which makes it the cheaper option to prototype on and to run at scale.

When were these models released and is this comparison current?

DeepSeek V4 shipped April 24, 2026, and Grok 4.5 became publicly available July 9, 2026. This comparison was last updated in July 2026, with all pricing fetched directly from each vendor's pricing page at that time and all benchmark figures attributed to Artificial Analysis or to each vendor's own reports. Independent scores and self-reported scores are kept separate throughout.