Claude Sonnet 4.6 vs GLM-4.6V
Wins 7 of 7 areas
Coding · Agents · Reasoning · Facts · Images and charts · Long documents · Following instructions
Wins 0 of 7 areas
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Claude Sonnet 4.6 is the stronger all-rounder.GLM-4.6V is cheaper.
Scores updated · 17 tests both models report · How we compare
Where each one wins
Tests won in each of the seven areas where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
- ReasoningHard problems that need careful thinking30Claude Sonnet 4.63 of 3 tests
- CodingWriting and fixing software20Claude Sonnet 4.62 of 2 tests
- FactsGetting facts right instead of making them up20Claude Sonnet 4.62 of 2 tests
- Images and chartsUnderstanding pictures, charts and video20Claude Sonnet 4.62 of 2 tests
- AgentsCarrying out multi-step tasks on its own10Claude Sonnet 4.61 of 1 test
- Long documentsFinding answers in very long texts10Claude Sonnet 4.61 of 1 test
- Following instructionsDoing exactly what it is asked10Claude Sonnet 4.61 of 1 test
Agents, long documents and following instructions rest on a single test each.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
GLM-4.6V costs 93% less for the same work.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Where Claude Sonnet 4.6 pulls ahead
- Hard command-line tasks in a real terminalTerminal-Bench Hard+38.6points ahead
- Reasons across sets of long documentsAA-LCR+31.3points ahead
- Real work tasks from 44 professionsGDPVal+31.2points ahead
Where GLM-4.6V pulls ahead
No clear win on a test scored out of 100.
Every test, side by side
All 17 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingClaude Sonnet 4.6
- Terminal-Bench HardClaude Sonnet 4.6 by 38.65314.4+38.6
- SciCodeClaude Sonnet 4.6 by 19.750.130.4+19.7
ReasoningClaude Sonnet 4.6
- Humanity's Last ExamClaude Sonnet 4.6 by 2433.69.6+24
- GPQA DiamondClaude Sonnet 4.6 by 15.687.571.9+15.6
- CritPtClaude Sonnet 4.6 by 3.13.10+3.1
FactsClaude Sonnet 4.6
- AA-Omniscience · AccuracyClaude Sonnet 4.6 by 24.740.916.2+24.7
- AA-Omniscience · Non-hallucinationClaude Sonnet 4.6 by 3.151.648.5+3.1
Images and chartsClaude Sonnet 4.6
- MMMU-ProClaude Sonnet 4.6 by 24.773.348.6+24.7
- LMArena · VisionClaude Sonnet 4.6 by 120 rating points12831163+120 rating
Long documentsClaude Sonnet 4.6
- AA-LCRClaude Sonnet 4.6 by 31.38048.7+31.3
Following instructionsClaude Sonnet 4.6
- IFBenchClaude Sonnet 4.6 by 26.556.630.1+26.5
Other results5 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- τ²-Bench Telecom (AA run)Claude Sonnet 4.6 by 44.175.731.6+44.1
- Artificial Analysis Coding IndexClaude Sonnet 4.6 by 43.36319.7+43.3
- AA-OmniscienceClaude Sonnet 4.6 by 39.112.2-26.9+39.1
- AA IntelligenceClaude Sonnet 4.6 by 18.930.111.2+18.9
- AA Agentic IndexClaude Sonnet 4.6 by 15.633.117.5+15.6
Questions people ask
Which is better, Claude Sonnet 4.6 or GLM-4.6V?
Claude Sonnet 4.6 wins all seven areas where both have results: coding, agents, reasoning, facts, images and charts, long documents and following instructions. GLM-4.6V wins none, but costs 93% less.
Which is better for coding?
Claude Sonnet 4.6. It wins 2 of the 2 coding tests both models report; GLM-4.6V wins none.
Which is cheaper?
Claude Sonnet 4.6 costs $3.00 per million input tokens and $15.00 per million output tokens; GLM-4.6V costs $0.30 and $0.90. That makes GLM-4.6V about 93% cheaper for the same work.
How do you compare the two?
We use the 17 benchmark tests both models have published scores on. The verdict counts the 12 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 5 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.