MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Claude Opus 4.8 vs GLM-5.2

Claude Opus 4.8 wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorAnthropic / Zhipu AI
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Claude Opus 4.8 when coding depth, long-context reliability matter most; choose GLM-5.2 when lower cost, lower latency, fine-tuning/ecosystem is the priority.

Choose Claude Opus 4.8 if you…

  • Previous flagship still capable
  • Well-proven stability
  • Best for: Deep reasoning, Long documents, Coding

Choose GLM-5.2 if you…

  • Strong open-source ecosystem
  • Good Chinese
  • Mature enterprise services
  • Best for: Chinese, Open source, Coding
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Claude Opus 4.8 Higher overall
Anthropic · #5 overall
Overall81
Coding87
Multimodal89
VS
GLM-5.2
Zhipu AI · #10 overall
Overall73
Coding66
Multimodal79

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionClaude Opus 4.8GLM-5.2Verdict
VendorAnthropic (US)Zhipu AI (CN)Different vendors
Released2026.052026.06GLM-5.2 is newer
Overall (rank)81 · #573 · #10Claude Opus 4.8 +8
Coding87 · #466 · #15Claude Opus 4.8 +21
Multimodal89 · #679 · #12Claude Opus 4.8 +10
Context window1M128KClaude Opus 4.8 larger
Max output128K64KGLM-5.2 longer
Effective-context9688Claude Opus 4.8 more reliable
Input $/1M$5$1.4GLM-5.2 cheaper
Output $/1M$25$4.4GLM-5.2 cheaper
Cache discount90% offnoneClaude Opus 4.8 deeper
Speed~40 tok/s~50 tok/sGLM-5.2 faster
TTFT1.0s0.6sGLM-5.2 snappier
Function calling8880Claude Opus 4.8 ahead
Refusal rate~9%~8%GLM-5.2 less restrictive
English9470Claude Opus 4.8
Chinese7890GLM-5.2
Modalitiestext, imagetext, imageSame
Open weightsNoYesGLM-5.2 is open
Fine-tuningNoYes
Free tierNo free tierChatGLM free; free quota on the open platform
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesGLM-5.2

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Claude Opus 4.8 leads the overall aggregate by 8 points (81 vs 73). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GLM-5.2 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a decisive gap: Claude Opus 4.8 scores 87 against 66. On multi-file edits, SWE-style tickets and long-horizon agent loops Claude Opus 4.8 needs fewer correction turns; GLM-5.2 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Claude Opus 4.8 leads multimodal 89 vs 79. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

GLM-5.2 is faster in interactive use: ~50 tok/s with 0.6s TTFT versus ~40 tok/s with 1.0s TTFT (about 1.2× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 1M for Claude Opus 4.8 and 128K for GLM-5.2. Effective-context scores point the same way as window size — Claude Opus 4.8 is ahead on usable recall (96 vs 88), so prefer it for long-document work where details cannot be missed.

Price & total cost

GLM-5.2 is the cheaper API at $1.4/$4.4 versus Claude Opus 4.8 at $5/$25 per 1M input/output tokens — list input is about 3.6× lower. Cache discounts (Claude Opus 4.8 90%) shift the effective bill, worked out below.

Chinese vs English

English: Claude Opus 4.8 94 vs GLM-5.2 70. Chinese: 78 vs 90. For Chinese-language production, GLM-5.2 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Claude Opus 4.8 88 vs GLM-5.2 80, so Claude Opus 4.8 has the edge on structured tool use. Fine-tuning is available from GLM-5.2. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthClaude Opus 4.8GLM-5.2Gap
List priceno cache applied$1,250100M in × $5  +  30M out × $25$272100M in × $1.4  +  30M out × $4.44.60×gap
With caching90% of inputs cache-hit$84590M cached in × $0.5  +  10M in × $5  +  30M out × $25$272no published cache discount3.11×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Claude Opus 4.8 (coding 87 vs 66).
IF you serve real-time users and latency is a product KPI  →  choose GLM-5.2 (~50 tok/s, 0.6s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose GLM-5.2 ($$1.4/$$4.4 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Claude Opus 4.8 (effective context 96 vs 88).
07

Frequently asked questions

Is Claude Opus 4.8 worth the higher price over GLM-5.2?
At list the input rate is 3.6x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Claude Opus 4.8's stronger dimensions protect revenue; for routine volume GLM-5.2 is the economical pick.
Which is better for agentic coding?
Claude Opus 4.8 is decisively stronger for coding (87 vs 66 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; GLM-5.2 is fine for routine scripts.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Claude Opus 4.8 is about $845/month and GLM-5.2 about $272/month after cache discounts ($1,250 and $272 at list).
Does the bigger context window actually matter?
Nominal windows are Claude Opus 4.8 (1M) and GLM-5.2 (128K), but usable recall follows the effective-context score (96 vs 88). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
GLM-5.2 is the Chinese model (Chinese score 90, domestic cloud, possible private deployment) while Claude Opus 4.8 is the global model (Chinese 78, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
GLM-5.2 ships open weights and can be self-hosted (GPU permitting) for data control; Claude Opus 4.8 is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.