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Model comparison

GLM 5.3 vs Grok 4.6

Z.ai against SpaceXAI, compared on context, price, and verified benchmark results.

Catalog record checked August 14, 2026; individual provider fields may change.

Z.ai

GLM 5.3

Frontier

vs

SpaceXAI

Grok 4.6

Frontier

AI model capability comparison
SpecificationGLM 5.3Grok 4.6
ProviderZ.aiSpaceXAI
TierFrontierFrontier
Context windowWinner: 1M500K
Max output128KNot verified
Input / 1M tokensNot verified$2
Output / 1M tokensNot verified$6
WeightsClosedClosed
ParametersNot disclosedNot disclosed
Reasoning levelslow, high, maxlow, medium, high, xhigh
Modalitiestexttext, image
ReleasedAugust 14, 2026August 12, 2026
Artificial Analysis Intelligence Index [high] (2026-08-12)Not verified61

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Pricing tiers: GLM 5.3: Direct `glm-5.3` API is documented as coming soon and is not on the Z.ai per-token table (do not assume GLM 5.2's $1.40/$4.40). Today it is on every GLM Coding Plan: points-based quota (input / cached input / output). Off-peak is 50% of standard points. Peak is Monday–Friday 14:00–18:00 UTC+8. Coding Plan requests for GLM-5.2/5.1 are routed to 5.3. · Grok 4.6: Base tier is $2/$6 per million tokens; prompts at or above 200K tokens are priced at $4/$12. Cached input is $0.50 / $1.00. Web/X search tool calls bill separately.

Frontier

GLM 5.3

Z.ai's 2026-08-14 Coding Plan flagship — same base as GLM 5.2, with the documented gains from post-training only. Direct token API and open weights are not published yet.

Best for

  • GLM Coding Plan agent work
  • Long-horizon coding in Claude Code / OpenCode / Cline
  • Teams already on Z.ai subscriptions

Watch out

Thinking cannot be disabled (`thinking.type: disabled` fails); `reasoning_effort` is low / high / max (default max). Open weights are promised about two weeks after launch pending safety review — not a downloadable checkpoint today. Vendor DeepSWE v1.1 66.9 used mini-swe-agent at 400K context, not the public Datacurve v1.1 board (snapshot generated 2026-08-13, before this launch).

Frontier

Grok 4.6

SpaceXAI's current code and chat default — same $2/$6 list as Grok 4.5, with a 500K context window and image input.

Best for

  • Coding agents
  • Chat and knowledge work
  • Cost-sensitive frontier work

Watch out

Token rates double above a 200K-token prompt. DeepSWE 1.1 best-effort for this family is the xhigh row at 66.7% (the catalog table rounds Pass@1 to 67%), not the higher-scoring medium config.

Benchmark

DeepSWE 1.1 in context

Only Grok 4.6 has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

Claude Opus 5 [max]

74%

Rows shown

24

Highest published reasoning effort per model (not best Pass@1)

Snapshot date

2026-08-13

Mirrored from deepswe.datacurve.ai

DeepSWE 1.1 pass@10%16%32%48%64%80%$0$4.50$9.00$13.50$18.00$22.50$27.00Avg cost per taskClaude Opus 5 [max]: 74% · $11.84 · 118k tokens · 99 stepsClaude Opus 5GPT-5.6 Sol [max]: 73% · $8.39 · 60k tokens · 61 stepsGPT-5.6 SolClaude Fable 5 [max]: 70% · $21.63 · 119k tokens · 88 stepsClaude Fable 5GPT-5.6 Terra [max]: 70% · $4.95 · 72k tokens · 76 stepsGPT-5.6 TerraKimi K3 [max]: 69% · $4.65 · 82k tokens · 98 stepsKimi K3GPT-5.6 Luna [max]: 67% · $3.03 · 73k tokens · 102 stepsGPT-5.6 LunaGPT-5.5 [xhigh]: 67% · $7.23 · 46k tokens · 82 stepsGPT-5.5Grok 4.6 [xhigh]: 67% · $5.50 · 71k tokens · 87 stepsGrok 4.6Gemini 3.7 Flash [high]: 65% · $2.18 · 107k tokens · 125 stepsGemini 3.7 FlashDeepSeek V4-Pro [max]: 63% · $0.24 · 106k tokens · 155 stepsDeepSeek V4-ProClaude Opus 4.8 [max]: 59% · $13.22 · 135k tokens · 120 stepsClaude Opus 4.8Qwen3.8-Max [xhigh]: 58% · $3.73 · 95k tokens · 111 stepsQwen3.8-MaxMuse Spark 1.2 [xhigh]: 55% · $3.70 · 99k tokens · 101 stepsMuse Spark 1.2Claude Sonnet 5 [max]: 54% · $26.40 · 214k tokens · 268 stepsClaude Sonnet 5Grok 4.5 [high]: 54% · $2.42 · 36k tokens · 61 stepsGrok 4.5DeepSeek V4-Flash [max]: 53% · $0.10 · 108k tokens · 153 stepsDeepSeek V4-FlashMuse Spark 1.1 [xhigh]: 53% · $2.36 · 74k tokens · 96 stepsMuse Spark 1.1GPT-5.4 [xhigh]: 52% · $5.65 · 71k tokens · 70 stepsGPT-5.4Gemini 3.6 Flash [high]: 47% · $4.42 · 96k tokens · 117 stepsGemini 3.6 FlashGLM 5.2 [max]: 44% · $3.92 · 78k tokens · 129 stepsGLM 5.2Gemini 3.5 Flash [high]: 36% · $3.45 · 76k tokens · 105 stepsGemini 3.5 FlashKimi K2.7 Code: 31% · $2.82 · 59k tokens · 149 stepsKimi K2.7 CodeClaude Sonnet 4.6 [high]: 30% · $5.52 · 76k tokens · 134 stepsClaude Sonnet 4.6Gemini 3.1 Pro [high]: 12% · $2.14 · 28k tokens · 76 stepsGemini 3.1 Pro

Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#ModelPass@1Cost / taskTokens / taskSteps / task
1Claude Opus 5 [max]74%$11.84118k99
2GPT-5.6 Sol [max]73%$8.3960k61
3Claude Fable 5 [max]70%$21.63119k88
4GPT-5.6 Terra [max]70%$4.9572k76
5Kimi K3 [max]69%$4.6582k98
6GPT-5.6 Luna [max]67%$3.0373k102
7GPT-5.5 [xhigh]67%$7.2346k82
8Grok 4.6 [xhigh]67%$5.5071k87
9Gemini 3.7 Flash [high]65%$2.18107k125
10DeepSeek V4-Pro [max]63%$0.24106k155
11Claude Opus 4.8 [max]59%$13.22135k120
12Qwen3.8-Max [xhigh]58%$3.7395k111
13Muse Spark 1.2 [xhigh]55%$3.7099k101
14Claude Sonnet 5 [max]54%$26.40214k268
15Grok 4.5 [high]54%$2.4236k61
16DeepSeek V4-Flash [max]53%$0.10108k153
17Muse Spark 1.1 [xhigh]53%$2.3674k96
18GPT-5.4 [xhigh]52%$5.6571k70
19Gemini 3.6 Flash [high]47%$4.4296k117
20GLM 5.2 [max]44%$3.9278k129
21Gemini 3.5 Flash [high]36%$3.4576k105
22Kimi K2.7 Code31%$2.8259k149
23Claude Sonnet 4.6 [high]30%$5.5276k134
24Gemini 3.1 Pro [high]12%$2.1428k76

DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

Common questions

GLM 5.3 vs Grok 4.6

Answered from the verified figures on this page rather than general guidance.

Which has the larger context window, GLM 5.3 or Grok 4.6?

GLM 5.3 accepts 1M tokens against 500K for Grok 4.6. This only matters if you routinely send very long documents or large codebases.

Do GLM 5.3 and Grok 4.6 support the same reasoning levels?

GLM 5.3 exposes low, high, max, while Grok 4.6 exposes low, medium, high, xhigh.

Should I use GLM 5.3 or Grok 4.6?

Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.3 suits glm coding plan agent work; Grok 4.6 suits coding agents.

Next step

Choosing between them

Tier and workload decide this more reliably than a leaderboard position does.

If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.

If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.