Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.6 (Fast) vs GLM 5 Turbo
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Opus 4.6 (Fast) wins on 3/3 benchmarks
Claude Opus 4.6 (Fast) wins 3 of 3 shared benchmarks. Leads in speed.
Category leads
speed·Claude Opus 4.6 (Fast)
Hype vs Reality
Attention vs performance
Claude Opus 4.6 (Fast)
#150 by perf·no signal
GLM 5 Turbo
no bench·no signal
Best value
Claude Opus 4.6 (Fast)
Claude Opus 4.6 (Fast)
0.5 pts/$
$90.00/M
GLM 5 Turbo
—
$2.60/M
Vendor risk
Who is behind the model
Anthropic
$380.0B·Tier 1
z-ai
private · undisclosed
Head to head
3 benchmarks · 2 models
Claude Opus 4.6 (Fast)GLM 5 Turbo
Artificial Analysis · Agentic Index
Claude Opus 4.6 (Fast) leads by +4.4
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Claude Opus 4.6 (Fast)
67.6
GLM 5 Turbo
63.1
Artificial Analysis · Coding Index
Claude Opus 4.6 (Fast) leads by +3.9
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Claude Opus 4.6 (Fast)
48.1
GLM 5 Turbo
44.2
Artificial Analysis · Quality Index
Claude Opus 4.6 (Fast) leads by +14.9
Claude Opus 4.6 (Fast)
53.0
GLM 5 Turbo
38.1
Full benchmark table
| Benchmark | Claude Opus 4.6 (Fast) | GLM 5 Turbo |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 67.6 | 63.1 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 48.1 | 44.2 |
Artificial Analysis · Quality Index | 53.0 | 38.1 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $30.00 | $150.00 | 1.0M tokens (~500 books) | $600.00 | |
| $1.20 | $4.00 | 262K tokens (~131 books) | $19.00 |