Grok 4.6 vs GLM-5.3

head-to-head

what do the benchmarks actually say?

On SWE-bench Verified — real, human-validated GitHub issues resolved end-to-end — Grok 4.6 posts 95.6% against 95.4% for GLM-5.3, a 0.2-point gap. Verified is the closest public proxy for "can it fix a real bug in a real repo without help", which is why it anchors our ranking.

A few points either way is real but not decisive: within that band, the agent scaffolding around the model — how it retrieves files, runs tests, and retries — often matters as much as the base model. Treat the gap as a lean, not a verdict.

which is cheaper to run?

Public per-token pricing isn't confirmed for both models, so we don't print a cost comparison yet.

when to pick each

how were these scores verified?

We only print a number once it's confirmed against a primary source or an independent evaluation, and each row on our leaderboard records which kind it is:

• Grok 4.6: Independent (vals.ai, benchmark updated 2026-08-12, mini-swe-agent bash-only harness): SWE-bench Verified 95.60% ±0.92. Entered ranked 2026-08-13, one day after launch, and it enters near the top: 4th of the 82 systems vals.ai has run, behind Claude Opus 5 (97.00% ±0.76), DeepSeek V4 Pro 0813 (96.40% ±0.83) and GPT-5.6 Sol (96.20% ±0.86). Read the gap to Claude Fable 5 below it (95.00% ±0.98) as a tie: 0.6 points against a pooled SEM of about 1.34 is well inside the margin of error, and we rank Grok 4.6 higher only because it scored higher. Same against Sol above it, a 0.6-point gap. This is the second time running that xAI shipped a Grok with no SWE-bench Verified number of its own and vals.ai supplied one: Grok 4.5 waited nine days, Grok 4.6 waited one. SpaceXAI still publishes no SWE-bench Verified or SWE-bench Pro figure, so the ranked score here is entirely vals.ai's measurement with no vendor claim to disclose against. What xAI did publish, all vendor-reported on its own table: AA Intelligence Index 61, CursorBench v3.2 69.9%, DeepSWE v1.1 65.9%, FrontierCode v1.1 Extended 61.3%, APEX-Agents 57.5%, APEX-SWE 56.4%, Terminal-Bench v3.0 26%, GDPVal-AA v2 1753, AA-Briefcase 1577, Harvey LAB (Vals) 15.8%. That table's shape still holds and is worth reading against this score: Grok 4.6 takes the best result on the three knowledge-work evals and loses the two hardest agentic-coding ones by wide margins (DeepSWE 65.9% vs Sol's 73%, Terminal-Bench v3.0 26% vs Sol's 34.6%). Terminal-Bench v3.0 is NOT comparable to the 83.3% TB2.1 figure on the Grok 4.5 row: on v3.0 Grok 4.5 scores 15.7% and the whole field tops out near 34%. On cost it is the standout of the top five, $0.78 per test against $1.29 for Opus 5, $1.15 for Sol and $2.05 for Fable 5, though it is also the slowest of them at 604s. Pricing unchanged from Grok 4.5 at $2/$6 per 1M. See /p/grok-4-6-frontier-benchmarks-terminal-bench-gap/.
• GLM-5.3: Independent (vals.ai, 2026-08-20 sweep, mini-swe-agent bash-only harness): SWE-bench Verified 95.4% ±0.94, 6th of 86 systems. Left the verifying queue on 2026-08-20, six days after launch. Z.ai published NO SWE-bench Verified figure of its own, so this rank rests entirely on the independent run, which is the cleanest kind of row on this board. Read it as tied with GPT-5.6 Terra (95.4% ±0.94), Grok 4.6 (95.6%) and Claude Fable 5 (95.0%); pooled SEM is about 1.3 points and every gap is inside it. That result is striking given the release is post-training only, on the same base model as GLM 5.2, which is ranked far below on an independent 82.8%. Vendor numbers from the Aug 14 2026 release post, run mostly inside a Claude Code 2.1.207 harness at max effort rather than a neutral one: Terminal-Bench 3.0 28.3 (up from GLM-5.2’s 4.6), DeepSWE v1.1 66.9 (from 46.2), Agents’ Last Exam 28.5 (from 23.8). Its headline claims were cyber rather than coding: CyberGym 84.5% and ExploitBench 54.4%. Marked closed because the weights are NOT out: Z.ai said roughly two weeks after launch pending safety hardening, with no license announced, so the open-weights flag flips only when they actually land. See /p/glm-5-3-cybergym-open-weights-delayed/.

Full reviewsGrok 4.6, decodedGLM-5.3, decoded

Ranked on our AI Coding Leaderboard, updated 2026-08-20. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.