A note on the keyword this article was originally scoped for: the original brief asked for a comparison against “Grok 4.6.” As of publication, Grok 4.6 has only been verbally confirmed by Elon Musk in a reply on X—no benchmarks, pricing, or release date exist for it. Publishing invented scores for an unreleased model would violate basic accuracy standards, so Aizolo instead compares the two models that are actually shipping and benchmarked: Grok 4.5 (xAI/SpaceXAI, released July 8, 2026) and GPT-5.6 (OpenAI, released July 9, 2026). We’ll update this article the moment Grok 4.6 ships with real numbers.

Grok 4.5 vs GPT-5.6: Full Benchmark Comparison

Two frontier models shipped one day apart in July 2026.

Grok 4.5 launched July 8. GPT-5.6 Sol followed July 9.

The timing wasn’t coincidental. It was competitive.

Within hours, benchmark charts from both labs were circulating on X, each drawn from vendor-reported numbers rather than reproduced, third-party runs.

This article works through what’s actually been published, by whom, and what it means if you’re choosing a model for coding, reasoning, or agentic work.

We’re not comparing Grok 4.5 to a hypothetical “Grok 4.6.” That model has been confirmed only in a one-line reply from Elon Musk, with zero benchmark data attached. Comparing it to GPT-5.6 today would mean fabricating numbers, so we don’t.

What follows is grounded in official documentation from xAI/SpaceXAI and OpenAI, plus independent evaluations from Artificial Analysis, Snorkel AI, and other third-party benchmark providers.

Where sources disagree, we say so. Where a benchmark doesn’t cover one of the two models, we say that too.

Model Overview: Grok 4.5 and GPT-5.6

Grok 4.5 is the first model released under SpaceXAI, the entity formed when SpaceX absorbed xAI earlier in 2026.

It’s also the first xAI model co-trained on data from Cursor, the AI coding assistant that SpaceXAI agreed to acquire in a deal reported at $60 billion.

xAI positions Grok 4.5 as its strongest model yet for coding, agentic tasks, and knowledge work, built on a 1.5-trillion-parameter V9 foundation and trained on tens of thousands of Nvidia GB300 GPUs.

Elon Musk described it publicly as “an Opus-class model, but faster, more token-efficient and lower cost,” and separately said internal testing put it “roughly comparable to Opus 4.7, but much faster.”

Those are vendor framing claims, not neutral benchmark conclusions. The published scores, covered below, tell a more mixed story.

GPT-5.6 is OpenAI’s newest model family, previewed June 26, 2026, and given general availability July 9, 2026.

Unlike prior GPT generations, which shipped as a single model with an adjustable reasoning dial, GPT-5.6 arrives as three separate tiers: Sol (flagship), Terra (balanced, lower cost), and Luna (fastest, cheapest).

OpenAI’s own launch materials emphasize coding, science, and cybersecurity capability gains, paired with what the company describes as its most advanced safety stack to date.

Notably, GPT-5.6’s initial rollout was restricted at the request of the U.S. government, limiting early access to selected trusted partners before broader API availability. OpenAI stated it does not want that kind of restriction to become standard practice going forward.

Release Timeline and Context

Getting the sequence right matters here, because it changes which benchmarks are even comparable.

• June 26, 2026 — OpenAI previews GPT-5.6 Sol as a limited preview to trusted partners, following a U.S. government request to restrict early rollout.
• July 8, 2026 — SpaceXAI releases Grok 4.5 publicly, with official benchmarks compared against GPT-5.5 and Claude Opus 4.8 — not GPT-5.6, since GPT-5.6’s full release hadn’t happened yet.
• July 9, 2026 — GPT-5.6 (Sol, Terra, Luna) reaches general availability across ChatGPT, Codex, and the OpenAI API.

That one detail — xAI benchmarking against GPT-5.5, not GPT-5.6, because of the release-order gap — is why several of the head-to-head numbers in circulation are third-party estimates rather than either lab’s own chart.

We flag which is which throughout this article.

Architecture and Training Philosophy

Grok 4.5 runs on a 1.5-trillion-parameter V9 foundation model. xAI has said a larger, roughly 2-trillion-parameter V-series model is still in training, targeting an August 2026 release — that would presumably be the “Grok 4.6” or next-generation model, though nothing is confirmed.

The defining architectural choice in Grok 4.5 is the Cursor training data. xAI reinforced the model on hundreds of thousands of multi-step software engineering tasks, aimed specifically at long-running coding and terminal agents.

GPT-5.6’s architectural story is the tier system itself. Instead of one model with a reasoning-effort slider, OpenAI ships three durable product lines: Sol for maximum capability, Terra for balanced cost/performance, and Luna for high-volume, low-latency work.

OpenAI frames this as letting each tier improve independently over time, rather than replacing the whole family with each new generation — though whether that holds is genuinely untested; OpenAI could just as easily ship GPT-5.7 and start over.

GPT-5.6 also introduces max and ultra reasoning modes on top of the tier system, plus cache-write pricing, a first for an OpenAI model family.

Coding Benchmarks

Coding is where both companies chose to make their loudest claims, and it’s also where the published numbers disagree most across sources — largely because of that release-timing gap.

Terminal-Bench 2.1 (command-line, multi-step agentic tasks):

xAI’s own launch chart puts Grok 4.5 at 83.3%, just behind GPT-5.5 at 83.4% and Claude Fable 5 at 84.3%.

OpenAI’s GPT-5.6 materials report Sol at 88.8% (base) and 91.9% (Sol Ultra) — a clear jump over GPT-5.5’s 83.4%, according to OpenAI’s own system card figures.

Because Grok 4.5’s official chart predates GPT-5.6’s release, there’s no vendor-published Grok-4.5-vs-GPT-5.6 Terminal-Bench row from either company. The comparison above is assembled from each lab’s separate charts, not a shared table.

SWE-bench Pro:

Grok 4.5 scored 64.7%, according to xAI’s launch materials — ahead of GPT-5.5’s 58.6%, but behind Claude Opus 4.8 (69.2%) and Claude Fable 5 (80.4%).

We could not find an official GPT-5.6 SWE-bench Pro score in OpenAI’s published materials at the time of writing. If your workflow depends specifically on this benchmark, verify directly with OpenAI’s latest documentation before deciding.

DeepSWE 1.1:

Reported figures vary by source here, which is a useful reminder that even “official” benchmark reporting differs depending on which chart or press cycle it’s pulled from. One set of figures has Grok 4.5 at 53%, trailing GPT-5.5’s 67%, Claude Opus 4.8’s 59%, and Claude Fable 5’s 70%. A separate report puts the Grok-vs-GPT-5.5 gap at roughly 14 points in GPT-5.5’s favor on this specific benchmark. Either way, DeepSWE is the one major coding benchmark where GPT models have a clear, multi-source-confirmed lead over Grok 4.5.

Token efficiency:

This is where Grok 4.5’s coding story gets genuinely interesting, independent of raw accuracy.

xAI reports Grok 4.5 uses roughly 4.2 times fewer output tokens than Claude Opus 4.8 on comparable SWE-bench Pro tasks (about 15,954 tokens on average). Independent reporting has pegged the cost of a typical Grok 4.5 coding task at $2.49, versus $5.07 for GPT-5.5 and $11.80 for Fable 5 on Claude Code.

We don’t have an equivalent, independently verified cost-per-task figure for GPT-5.6 Sol specifically at time of writing, since it launched only a day after these comparisons were published.

Reasoning and Knowledge Benchmarks

Artificial Analysis Intelligence Index, a third-party composite score independent of both vendors, is the closest thing to a neutral cross-model comparison currently available:

Artificial Analysis places Grok 4.5 at 54 (ranked #4 overall). GPT-5.6 Sol (max configuration) scores 59, one point behind Claude Fable 5, but at roughly a third of Fable 5’s cost. GPT-5.6 Terra (max) scores 55, and Luna (max) scores 51, at progressively lower cost-per-task than Sol.

By this specific, independently-run index, GPT-5.6 Sol outscores Grok 4.5 on raw intelligence — a genuinely different conclusion from the vendor-chart coding comparisons above, which is exactly why distinguishing “official” from “independent” evaluation matters.

GDPVal+ (Snorkel AI), which evaluates roughly 2,000 real-world, expert-level professional tasks across sectors:

Grok 4.5 posted a 29% mean pass rate, ahead of GPT-5.5 at 22% and Claude Opus 4.8 at 21%. The gap was widest in legal and healthcare task categories. Snorkel notes this is an evaluation against GPT-5.5, not GPT-5.6, so treat it as informative about Grok’s trajectory rather than a direct GPT-5.6 comparison.

Hallucination rate (AA-Omniscience Index):

Grok 4.5’s accuracy on this index reportedly rose from 35% to 52% over its predecessor, but its hallucination rate also rose, from 25% to 54% — higher than the roughly 52% rate reported for comparable rival models. In plain terms: it knows more, and it’s also more confidently wrong more often. That’s a real reliability consideration for anyone deploying it in domains where incorrect-but-confident answers are costly.

We don’t have a directly comparable, independently published hallucination figure for GPT-5.6 Sol at this time.

Agentic Workflows and Tool Use

Agents’ Last Exam (ALE), which evaluates long-running professional workflows across 55 fields, appears in OpenAI’s own GPT-5.6 release materials.

By OpenAI’s reported figures, all three GPT-5.6 tiers outperform every non-OpenAI model listed, with Sol beating Claude Fable 5 by roughly 13 points and Luna trailing Sol by only about 3 points despite costing a fraction as much.

Since this is a vendor-published comparison from OpenAI’s own launch chart, it’s worth treating with the same caution as xAI’s self-reported Grok charts — genuinely useful as a data point, but not independently reproduced.

BrowseComp, a benchmark for autonomous web research and information retrieval, is reported by OpenAI at 90.4% for Sol and 92.2% for Sol Ultra. We did not find a directly comparable Grok 4.5 BrowseComp score in xAI’s published materials.

Context handling in agent loops: Grok 4.5 runs at approximately 80 tokens per second and supports a 500,000-token context window.

GPT-5.6 Sol supports a larger 1-million-token context window, according to comparison data from DocsBot’s model-comparison tool, which cross-references both vendors’ published specs.

Cybersecurity and Safety Evaluations

This is an area where OpenAI published unusually detailed third-party evaluation data for GPT-5.6 Sol.

On ExploitBench, OpenAI reports Sol performs competitively with the Mythos Preview model while using roughly a third of the output tokens.

On ExploitGym, a benchmark built by UC Berkeley researchers in collaboration with OpenAI and other labs, OpenAI reports that Sol, Terra, and Luna all show meaningful cyber-capability improvements as reasoning effort increases.

OpenAI’s own system card classifies the GPT-5.6 family as “High” capability under its Preparedness Framework for both cybersecurity and biological/chemical risk categories — one tier below “Critical.”

In testing against Chromium and Firefox, OpenAI reports the model identified bugs and exploitation building blocks, but did not autonomously produce a full working exploit chain under the tested conditions.

Independently, SecureBio, a nonprofit focused on catastrophic biological risk, evaluated pre-release GPT-5.6 Sol checkpoints (with content filters disabled for assessment purposes) and reported some of the highest scores to date on several expert-level biology benchmarks, including 68.3% on World-Class Bio — about 9 points higher than GPT-5.5.

We could not locate equivalent, published third-party cybersecurity or biosecurity risk evaluations for Grok 4.5 at time of writing. That’s a genuine gap in the public record, not evidence either way about the model’s risk profile — it simply hasn’t been reported the way GPT-5.6’s has.

Context Window, Speed, and Multimodal Capability

Grok 4.5 accepts text and image input and produces text-only output, running a configurable reasoning process where “high” effort is the default and cannot be turned off.

GPT-5.6 Sol’s headline differentiator on this front is the larger 1-million-token context window, plus the addition of max and ultra reasoning tiers layered on top of the three-model system.

Pricing and Cost-per-Task

This is the least ambiguous part of the comparison, since both companies publish list pricing directly.

By list price, Grok 4.5 is roughly 4–5 times cheaper than GPT-5.6 Sol on a per-token basis, according to comparison data compiled by DocsBot. GPT-5.6 Terra and Luna narrow that gap considerably, with Luna landing close to Grok 4.5’s price point while trading away some of Sol’s peak capability.

If cost-per-completed-task rather than cost-per-token is what matters for your use case, the Snorkel and independent-reporting figures above (Grok 4.5 at roughly $2.49 per typical coding task, versus $5.07 for GPT-5.5) are the most concrete data currently public — though a directly equivalent GPT-5.6 figure hasn’t been independently published yet.

What the Benchmarks Don’t Show

Benchmarks tell you whether a model is capable under controlled, repeatable conditions. They don’t tell you how it behaves in your actual workflow.

Reproducibility. Several of the coding numbers cited across this comparison — on both sides — are vendor-reported, not independently reproduced at time of writing. Treat any single-source chart as a starting hypothesis, not a settled conclusion.

Reliability drift over long sessions. None of the published benchmarks fully capture how a model behaves across a multi-hour agent loop touching a messy real-world repository, inconsistent tool outputs, and ambiguous instructions. Grok 4.5’s rising hallucination rate on the AA-Omniscience Index is a hint that raw knowledge gains and reliability don’t always move together — worth testing directly rather than assuming from a leaderboard.

Latency under load. Published tokens-per-second figures are typically measured under favorable conditions. Production latency, especially for GPT-5.6 Sol’s “max” and “ultra” reasoning modes, which plan more before acting, will vary by workload.

Prompt robustness. How a model handles ambiguous, poorly specified, or adversarial prompts in your specific domain isn’t something any of these benchmarks directly measure.

The practical takeaway from every source we reviewed, including outlets favorable to each vendor, is the same: validate both models against your own repositories, tool schemas, and latency targets before committing to production routing.

Strengths, Weaknesses, and Best Use Cases

Grok 4.5 — strengths: Aggressive pricing, strong token efficiency on coding tasks, competitive SWE-bench Pro performance, tight integration with Cursor and the new Grok Build terminal agent.

Grok 4.5 — weaknesses: Smaller context window (500K vs. 1M), trails on DeepSWE 1.1, rising hallucination rate on knowledge tasks, fewer independently published safety evaluations in the public record.

GPT-5.6 Sol — strengths: Larger context window, strong Terminal-Bench and Agents’ Last Exam scores per OpenAI’s own charts, detailed independent biosecurity and cybersecurity evaluation data, tiered pricing via Terra and Luna for cost-sensitive workloads.

GPT-5.6 Sol — weaknesses: Meaningfully more expensive per token than Grok 4.5, initial rollout was access-restricted at the request of the U.S. government, and several headline benchmark claims are vendor-published rather than independently reproduced.

Best use case for Grok 4.5: high-volume agentic coding pipelines where cost-per-task and token efficiency compound across many tool calls, and where you’re already inside the Cursor or Grok Build ecosystem.

Best use case for GPT-5.6: workloads that need the largest available context window, teams that want tiered routing between Sol/Terra/Luna by task depth, and use cases (cybersecurity research, biosecurity-adjacent work) where OpenAI’s more extensively documented safety evaluation gives more confidence.

Who should wait: if your decision truly hinges on “Grok 4.6,” there’s nothing to evaluate yet beyond a name. A larger xAI V-series model is reportedly in training for an August 2026 release — worth revisiting this comparison once real numbers exist.

Comparison Tables

Feature Comparison

Winner by Category (based on cited sources only)

Note: several rows compare Grok 4.5 against GPT-5.5, not GPT-5.6, because that’s what the underlying source actually measured. We’ve kept that distinction explicit rather than blending the numbers.

Internal Linking

FAQ

Is Grok 4.6 available yet? No. As of this article’s publication, Grok 4.6 has only been confirmed verbally by Elon Musk on X, with no benchmarks, pricing, or release date published.

Which model is cheaper, Grok 4.5 or GPT-5.6? Grok 4.5 is significantly cheaper by list price: $2/$6 per million input/output tokens versus $5/$30 for GPT-5.6 Sol. GPT-5.6’s Terra and Luna tiers narrow that gap.

Which model has a bigger context window? GPT-5.6 Sol, at 1 million tokens, versus 500,000 tokens for Grok 4.5.

Does Grok 4.5 beat GPT-5.6 on coding benchmarks? It’s mixed, and the comparisons aren’t fully apples-to-apples because xAI’s official chart compared Grok 4.5 to GPT-5.5, not GPT-5.6, which launched a day later. Grok 4.5 leads on SWE-bench Pro against GPT-5.5; GPT-5.6 reports a strong Terminal-Bench 2.1 score, ahead of GPT-5.5’s own prior mark.

What is the Artificial Analysis Intelligence Index? An independent, third-party composite benchmark score that isn’t published by either xAI or OpenAI, used as a neutral cross-model reference point.

Is Grok 4.5 more likely to hallucinate than GPT-5.6? Grok 4.5’s hallucination rate reportedly rose to 54% on the AA-Omniscience Index. We don’t have a directly comparable, independently published figure for GPT-5.6 Sol at this time.

Which model is better for cybersecurity research? GPT-5.6 Sol has more extensively documented cybersecurity evaluation data, including OpenAI’s own ExploitBench/ExploitGym results and its Preparedness Framework classification. We could not find equivalent published evaluations for Grok 4.5.

What is GPT-5.6 Terra and Luna? Lower-cost tiers within the GPT-5.6 family. Terra is a balanced mid-tier option; Luna is the fastest and cheapest, intended for high-volume, low-latency work.

Does Grok 4.5 support image input? Yes, Grok 4.5 accepts text and image input, but produces text-only output.

Was GPT-5.6’s rollout restricted? Yes. OpenAI restricted initial GPT-5.6 access to selected trusted partners at the request of the U.S. government, citing safety concerns, before expanding to general availability.

Which model is trained with Cursor’s help? Grok 4.5, following SpaceXAI’s acquisition of Cursor, was co-trained on Cursor’s developer-workflow data.

Is there a neutral, third-party benchmark that includes both models? Yes — the Artificial Analysis Intelligence Index includes both, along with Snorkel AI’s GDPVal+ evaluation (though the latter compared Grok 4.5 to GPT-5.5, not GPT-5.6).

Should I wait for Grok 4.6 before choosing a model? Only if your timeline allows it. xAI is reportedly training a larger V-series model for an August 2026 release, but nothing about its benchmarks or pricing is confirmed yet.

References and Sources

• xAI / SpaceXAI official Grok 4.5 announcement and developer documentation
• OpenAI, “Previewing GPT-5.6 Sol” and GPT-5.6 System Card, deploymentsafety.openai.com
• Artificial Analysis, “GPT-5.6 has landed,” artificialanalysis.ai
• Snorkel AI, Grok 4.5 GDPVal+ evaluation, snorkel.ai
• SecureBio biosecurity evaluation of GPT-5.6 Sol
• TechCrunch, coverage of SpaceXAI’s Grok 4.5 launch
• Independent benchmark aggregation and comparison sites (DocsBot, BenchLM, CodingFleet, MindStudio, The AI Dude), used for cross-referencing vendor claims — flagged throughout as non-primary sources

Author bio

Jeevesh Tripathi AI Researcher, Aizolo Email: jeevesh@aizolo.com

Jeevesh Tripathi is an AI researcher at Aizolo focused on independent evaluation of frontier language models, benchmark methodology, and enterprise AI adoption. His work centers on separating vendor marketing claims from reproducible, third-party evaluation data, helping engineering and product teams make model-selection decisions grounded in evidence rather than launch-day hype. He writes regularly on model benchmarking, agentic coding workflows, and the practical gap between published scores and production performance.

Conclusion

There’s no clean, universal winner here, and the evidence doesn’t support pretending otherwise.

Choose Grok 4.5 if cost-per-task and token efficiency matter most, especially for high-volume agentic coding pipelines, and you’re comfortable working with a 500K context window and a model whose knowledge-benchmark hallucination rate has been trending upward.

Choose GPT-5.6 (in whichever tier fits your budget — Sol, Terra, or Luna) if you need the larger 1M-token context window, want more extensively documented safety and cybersecurity evaluation data, or your workload benefits from OpenAI’s tiered routing between capability levels.

Wait, and revisit this comparison, if what you actually want is Grok 4.6. It doesn’t exist yet in any evaluable form, and any article claiming otherwise is making numbers up.

The most consistent theme across every credible source reviewed for this piece: validate both current models against your own repositories, tools, and latency requirements before routing production traffic. Benchmarks are a starting point for that decision, not a substitute for it.