GLM 4.5V vs GPT-3.5 Turbo Instruct
GLM 4.5V and GPT-3.5 Turbo Instruct cost about the same per output token ($1.80 vs $2.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.80
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
GLM 4.5V or GPT-3.5 Turbo Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5V for output tokens 1.1x cheaper ($1.80 vs $2.00 per million) and a larger context window (66K against 4K tokens).
No benchmark has tested both yet. GLM 4.5V has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding; GPT-3.5 Turbo Instruct on METR time horizons. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5V has a AA Intelligence Index result, 7.6.
Directional only
Long documents
Listed context window
GLM 4.5V
GLM 4.5V takes 66K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
GLM 4.5V $0.0015, GPT-3.5 Turbo Instruct $0.0025 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, GPT-3.5 Turbo Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GLM 4.5V $1.80 against GPT-3.5 Turbo Instruct $2.00 per million output tokens, 1.1x apart.
- Context window
- GLM 4.5V takes 66K tokens, GPT-3.5 Turbo Instruct 4K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: coding | 1347 | Not measured | Only GLM 4.5V measured |
| LMArena Text: software and IT | 1353 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| METR time horizons | Not measured | 21.5% | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts | 1335 | Not measured | Only GLM 4.5V measured |
| LMArena Text: expert | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | 1357 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text: math | 1354 | Not measured | Only GLM 4.5V measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | Not measured | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only GLM 4.5V measured |
| LMArena Text: legal and government | 1338 | Not measured | Only GLM 4.5V measured |
| LMArena Text: business and finance | 1309 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | GLM 4.5V | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: instruction following | 1311 | Not measured | Only GLM 4.5V measured |
| LMArena Text: creative writing | 1294 | Not measured | Only GLM 4.5V measured |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | Not measured | Only GLM 4.5V measured |
| LMArena Text: non-English | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Chinese | 1337 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Spanish | 1336 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Russian | 1298 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Polish | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: longer queries | 1305 | Not measured | Only GLM 4.5V measured |
| LMArena Text: writing and language | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 28 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 4.5V costs less
Repository review
50K input + 3K output tokens
GLM 4.5V costs less
Agent loop
200K input + 10K output tokens
GLM 4.5V costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 4.5V | OpenAI GPT-3.5 Turbo Instruct |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 11, 2025 | September 28, 2023 |
| Context window | 66K | 4K |
| Input $/Mtok | $0.60 | $1.50 |
| Output $/Mtok | $1.80 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5V better than GPT-3.5 Turbo Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 1.1x cheaper ($1.80 vs $2.00 per million) and a larger context window (66K against 4K tokens).
Which is cheaper, GLM 4.5V or GPT-3.5 Turbo Instruct?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; GPT-3.5 Turbo Instruct costs $1.50 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or GPT-3.5 Turbo Instruct?
GLM 4.5V, with 66K tokens against 4K.
Has any benchmark tested both GLM 4.5V and GPT-3.5 Turbo Instruct?
No benchmark has tested both yet. GLM 4.5V has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding; GPT-3.5 Turbo Instruct on METR time horizons. Compare them on price and context instead. Data as of October 22, 2026.