Gemini 4 Carbon vs GLM-5
Full profiles: Gemini 4 Carbon (Google) and GLM-5 (Zhipu AI): every benchmark, price and rank.
Updated · Data from 7 sources
As of 29 Oct 2026, Gemini 4 Carbon and GLM-5 are neck and neck on quality (65.1 vs 60.2 on the BenchLeader Index, inside the ±7.1 margin), and GLM-5 is 5.2× cheaper at $1.55 vs $8.00 per 1M tokens blended. Gemini 4 Carbon leads in every category we measure both in. Gemini 4 Carbon streams faster.
Key facts (as of 29 Oct 2026)
- Gemini 4 Carbon scores 65.1 on the BenchLeader Index and GLM-5 scores 60.2 (BenchLeader, 29 Oct 2026).
- Gemini 4 Carbon costs $8.00 per 1M tokens blended (list price, 29 Oct 2026).
- GLM-5 costs $1.55 per 1M tokens blended (list price, 29 Oct 2026).
- Gemini 4 Carbon streams faster than GLM-5 (Artificial Analysis, 7-day median, 29 Oct 2026).
- Of the 5 benchmarks both models have, Gemini 4 Carbon leads 5 (BenchLeader, 29 Oct 2026).
Gemini 4 Carbon benchmarks high Google
GLM-5 benchmarks thinking Zhipu AIOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Gemini 4 Carbon | 72 vs 59 category score; Vibe Code Bench v1.1 89.1% vs 23.4% | High |
| Reasoning | Gemini 4 Carbon | 79 vs 68 category score; Humanity's Last Exam (AA) 60.4% vs 29.3% | High |
| Knowledge | Gemini 4 Carbon | 76 vs 57 category score | High |
| Long documents | Gemini 4 Carbon | 65 vs 63 category score; AA-LCR v1.1 79.3% vs 75.7% | Medium |
| Lowest cost | GLM-5 | $1.55 vs $8.00 per 1M blended; agentic coding session $0.073 vs $0.320 | – |
| Fastest first answer | GLM-5 | 46.2 s (GLM-5 (thinking)) vs 103.1 s (Gemini 4 Carbon (high)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Gemini 4 Carbon (high) and GLM-5 (thinking) are neck and neck on quality (65.1 vs 60.2, within the ±7.1 margin).
- Gemini 4 Carbon (high) is stronger in coding, knowledge, long context, reasoning.
- GLM-5 (thinking) is stronger in agents & tools, instruction following, maths.
- GLM-5 (thinking) is 5.2× cheaper ($1.55 vs $8.00 per 1M blended).
- Gemini 4 Carbon (high) streams 1.4× faster (100 vs 70 tokens per second).
At a glance
| Spec | Gemini 4 Carbon | GLM-5 |
|---|---|---|
| Developer | Zhipu AI | |
| Released | 28 Oct 2026 | 11 Feb 2026 |
| Context window (tokens) | 1,000,000 | 204,800 |
| Compared at | Gemini 4 Carbon (high) | GLM-5 (thinking) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $4.00 / $20.00 / – | $1.00 / $3.20 / $0.149 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Gemini 4 Carbon (high) | GLM-5 (thinking) | Leader |
|---|---|---|---|
| Reasoning | 78.9 | 68.2 | Gemini 4 Carbon |
| Coding | 71.7 | 59.1 | Gemini 4 Carbon |
| Agents & tools | – | 58.0 | not comparable |
| Maths | – | 63.2 | not comparable |
| Knowledge | 75.8 | 57.3 | Gemini 4 Carbon |
| Instruction following | – | 70.8 | not comparable |
| Long context | 64.5 | 62.6 | Gemini 4 Carbon |
Where sources disagree
Across the 5 benchmarks both have, Gemini 4 Carbon leads 5, GLM-5 leads 0.
| Overall index | Gemini 4 Carbon (high) | GLM-5 (thinking) | Leader |
|---|---|---|---|
| BenchLeader Index | 65.1 | 60.2 | neck and neck |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Gemini 4 Carbon (high) | GLM-5 (thinking) |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $1.55 |
| Input / output per 1M | $4.00 / $20.00 | $1.00 / $3.20 |
| Cached input per 1M | – | $0.149 |
| Price per task: chat reply | $0.0076 | $0.0014 |
| Price per task: summarize a 30-page report | $0.060 | $0.014 |
| Price per task: code edit | $0.054 | $0.011 |
| Price per task: agentic coding session | $0.320 | $0.073 |
| Price per task: structured extraction | $0.012 | $0.0026 |
| Cost to run a full benchmark suite | $7.76 | – |
| Output speed | 100 tok/s | 70 tok/s |
| Time to first answer | 103.1 s | 46.2 s |
| Time to a complete answer | 108.1 s | 53.4 s |
Each model is compared at its best-ranked reasoning-effort setting. Gemini 4 Carbon (high) scores 65.1 and takes 103 s to its first answer token. GLM-5 (thinking) scores 60.2 and takes 46 s to its first answer token; GLM-5 scores 54.9 in 2 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
79 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Gemini 4 Carbon (high) | GLM-5 (thinking) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 65.1 | 60.2 |
| Coding score | BenchLeader | 71.7 | 59.1 |
| Knowledge score | BenchLeader | 75.8 | 57.3 |
| Long context score | BenchLeader | 64.5 | 62.6 |
| Reasoning score | BenchLeader | 78.9 | 68.2 |
| Agents & tools score | BenchLeader | – | 58.0 |
| Instruction following score | BenchLeader | – | 70.8 |
| Maths score | BenchLeader | – | 63.2 |
| Blended price $/M | BenchLeader | $8.00 | $1.55 |
| Output speed | BenchLeader | 100 tok/s | 70 tok/s |
| Time to first answer | BenchLeader | 103.1 s | 46.2 s |
| Context window | BenchLeader | 1,000,000 tokens | 204,800 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 87.8%† |
| SimpleBench | SimpleBench | – | 53.2%† |
| Arena Hard Prompts | Arena (LMArena) | – | 1478† |
| GPQA Diamond (AA) | Artificial Analysis | – | 82.0% |
| Humanity's Last Exam (AA) | Artificial Analysis | 60.4% | 29.3% |
| HLE-Diamond (AA) | Artificial Analysis | 69.1% | – |
| GPQA Diamond (Vals) | Vals AI | – | 83.3% |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 75.0% |
| ARC-AGI-1 | ARC Prize | – | 44.7%† |
| ARC-AGI-2 | ARC Prize | – | 4.9%† |
| CritPt | Artificial Analysis | 36.0% | 2.0% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 63.0% | – |
| CL-bench | Epoch AI Benchmarking Hub | – | 18.7%† |
| ForecastBench | ForecastBench | – | 63.7%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | – | 72.1%† |
| SciCode | SciCode | 64.2% | – |
| WeirdML | WeirdML | – | 48.2%† |
| Arena Coding | Arena (LMArena) | – | 1498† |
| Arena WebDev | Arena (LMArena) | – | 1436† |
| LiveCodeBench | Vals AI | – | 81.9% |
| SWE-bench (Vals) | Vals AI | – | 71.4% |
| SWE-bench Verified (bash only) | SWE-bench | – | 72.8%† |
| SWE-bench Verified (any scaffold) | SWE-bench | – | 72.8%† |
| Vibe Code Bench v1.1 | Vals AI | 89.1% | 23.4% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 20.5%† |
Show all 79 benchmarks
| Metric | Publisher | Gemini 4 Carbon (high) | GLM-5 (thinking) |
|---|---|---|---|
| Coding | |||
| SWE Atlas: Refactoring | Scale AI SEAL | – | 24.2%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 28.7%† |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 765.6† |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 65.2% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 48.5% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 40.6% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 52.4%† |
| Terminal-Bench Hard | Artificial Analysis | – | 43.2% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 98.3% |
| τ²-bench | τ²-bench | – | 81.0% |
| τ³-bench Banking | τ²-bench | – | 9.8% |
| APEX-Agents (AA) | Artificial Analysis | – | 14.4% |
| Finance Agent v2.1 | Vals AI | 67.2% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 17.5% | – |
| Legal Research Bench | Vals AI | 55.8% | – |
| Tax Agent Bench | Vals AI | 48.9% | – |
| Terminal-Bench 2.0 (Vals) | Vals AI | – | 49.4% |
| Terminal-Bench 4.0 (Vals) | Vals AI | 63.1% | – |
| Vending-Bench 2 | Andon Labs | 16484.2† | 4432.1† |
| AutomationBench | Artificial Analysis | 77.8% | – |
| GDP.pdf (AA) | Artificial Analysis | 28.4% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1609 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 64.8% | – |
| Maths | |||
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 80.0%† |
| AIME (Vals) | Vals AI | – | 91.7% |
| AIME 2026 | MathArena | – | 96.7%† |
| HMMT February 2026 | MathArena | – | 86.4%† |
| USAMO 2026 | MathArena | – | 35.1%† |
| MathArena Apex | MathArena | – | 10.9%† |
| Arena Maths | Arena (LMArena) | – | 1443† |
| Knowledge | |||
| AA-Omniscience v1.1 | Artificial Analysis | 53.5 | – |
| MMLU-Pro | Vals AI | – | 86.0% |
| LegalBench | Vals AI | – | 84.1% |
| CorpFin | Vals AI | – | 62.9% |
| TaxEval | Vals AI | – | 70.0% |
| MedQA | Vals AI | – | 94.3% |
| CaseLaw v2 | Vals AI | – | 52.5% |
| Excel Modeling Benchmark v1.1 | Vals AI | 76.5% | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 58.3% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 88.6% | – |
| Instruction following | |||
| IFBench | Artificial Analysis | – | 72.3% |
| Arena Instruction Following | Arena (LMArena) | – | 1446† |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1457† |
| Arena Creative Writing | Arena (LMArena) | – | 1448† |
| Arena Multi-turn | Arena (LMArena) | – | 1472† |
| Arena Longer Queries | Arena (LMArena) | – | 1468† |
| Multimodal | |||
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 58.6%† | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 79.3% | 75.7% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 145.8† |
| AA Intelligence Index v5.0 | Artificial Analysis | 59.4 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 27.9 |
| AA Progress Index v1 | Artificial Analysis | 351.6 | 280.9 |
| Vals Index | Vals AI | 70.4 | – |
| Vals Finance Index | Vals AI | 66.3% | – |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): BenchLeader Index 60.7 → 60.2.
- GLM-5 (thinking): 1 new result (AA Progress Index v1); cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): 1 new result (τ³-bench Banking).
Related comparisons
- Gemini 4 Carbon vs Claude Opus 5.5
- Gemini 4 Carbon vs GPT-6 Astra
- Gemini 4 Carbon vs GPT-6.1 Sol
- GLM-5 vs Claude Opus 5.5
- GLM-5 vs GPT-6 Astra
- GLM-5 vs GPT-6.1 Sol
- GLM-5 vs Claude Fable 5.1
- Gemini 4 Argon vs GLM-5
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Gemini 4 Carbon vs GLM-5: questions
- Is Gemini 4 Carbon better than GLM-5?
- Gemini 4 Carbon (high) and GLM-5 (thinking) are neck and neck on quality (65.1 vs 60.2, within the ±7.1 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 29 Oct 2026, so check the category scores for your use.
- Gemini 4 Carbon vs GLM-5: which is cheaper, and will that change?
- GLM-5 is cheaper today: $1.55 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Gemini 4 Carbon or GLM-5 better for coding?
- Gemini 4 Carbon scores higher in coding (72 vs 59 on the category index, where 50 is average).
- Which is faster, Gemini 4 Carbon or GLM-5?
- Gemini 4 Carbon streams faster (100 vs 70 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- Gemini 4 Carbon scores higher on long-context benchmarks (65 vs 63). Gemini 4 Carbon accepts more context: 1,000,000 against 204,800 tokens for GLM-5.
More questions (3)
- Which has lower latency?
- GLM-5 answers first: 46.2 s against 103.1 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Do independent benchmarks agree on Gemini 4 Carbon vs GLM-5?
- Across the 5 benchmarks both have, Gemini 4 Carbon leads 5, GLM-5 leads 0.
- When was Gemini 4 Carbon released?
- Gemini 4 Carbon was released on 28 Oct 2026.