GPT-3.5 Turbo 16k vs Granite 4.2 8B
Granite 4.2 8B costs 20.0x less per output token than GPT-3.5 Turbo 16k ($0.20 vs $4.00 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
GPT-3.5 Turbo 16k or Granite 4.2 8B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Granite 4.2 8B on AA Coding Index, SciCode and LMArena Text: coding. 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 Granite 4.2 8B has a AA Intelligence Index result, 11.1.
Directional only
Agentic work
AA Agentic Index
No call
Only Granite 4.2 8B has a AA Agentic Index result, 1.3.
Directional only
Long documents
Listed context window
Granite 4.2 8B
Granite 4.2 8B takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.2 8B
GPT-3.5 Turbo 16k $0.0050, Granite 4.2 8B $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo 16k not benchmarked, Granite 4.2 8B 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
- GPT-3.5 Turbo 16k $4.00 against Granite 4.2 8B $0.20 per million output tokens, 20.0x apart.
- Context window
- GPT-3.5 Turbo 16k takes 16K tokens, Granite 4.2 8B 131K.
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 | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 22.4 | Only Granite 4.2 8B measured |
| SciCode | Not measured | 31.5% | Only Granite 4.2 8B measured |
| LMArena Text: coding | Not measured | 1378 | Only Granite 4.2 8B measured |
| LMArena Text: software and IT | Not measured | 1366 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.1 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.3 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 63.1% | Only Granite 4.2 8B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 9.7% | Only Granite 4.2 8B measured |
| LMArena Text: hard prompts | Not measured | 1327 | Only Granite 4.2 8B measured |
| LMArena Text: expert | Not measured | 1386 | Only Granite 4.2 8B measured |
| LMArena Text: hard prompts in English | Not measured | 1344 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 18 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1335 | Only Granite 4.2 8B measured |
| LMArena Text: medicine and healthcare | Not measured | 1332 | Only Granite 4.2 8B measured |
| LMArena Text: legal and government | Not measured | 1327 | Only Granite 4.2 8B measured |
| LMArena Text: business and finance | Not measured | 1322 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 16 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Granite 4.2 8B | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 45.0% | Only Granite 4.2 8B measured |
| LMArena Text | Not measured | 1319 | Only Granite 4.2 8B measured |
| LMArena Text: instruction following | Not measured | 1301 | Only Granite 4.2 8B measured |
| LMArena Text: creative writing | Not measured | 1235 | Only Granite 4.2 8B measured |
| LMArena Text: English | Not measured | 1337 | Only Granite 4.2 8B measured |
| LMArena Text: non-English | Not measured | 1300 | Only Granite 4.2 8B measured |
| LMArena Text: Chinese | Not measured | 1365 | Only Granite 4.2 8B measured |
| LMArena Text: Russian | Not measured | 1280 | Only Granite 4.2 8B measured |
| LMArena Text: multi-turn | Not measured | 1300 | Only Granite 4.2 8B measured |
| LMArena Text: longer queries | Not measured | 1324 | Only Granite 4.2 8B measured |
| LMArena Text: writing and language | Not measured | 1269 | Only Granite 4.2 8B measured |
| LMArena Text: entertainment and media | Not measured | 1271 | Only Granite 4.2 8B measured |
| Neither model has a result on the other 36 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
Granite 4.2 8B costs less
Repository review
50K input + 3K output tokens
Granite 4.2 8B costs less
Agent loop
200K input + 10K output tokens
Granite 4.2 8B 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 | OpenAI GPT-3.5 Turbo 16k | IBM Granite 4.2 8B |
|---|---|---|
| Maker | OpenAI | IBM Granite |
| Weights | No public weights listed | Open weights |
| Released | August 28, 2023 | August 31, 2026 |
| Context window | 16K | 131K |
| Input $/Mtok | $3.00 | $0.08 |
| Output $/Mtok | $4.00 | $0.20 |
| 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 GPT-3.5 Turbo 16k better than Granite 4.2 8B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo 16k or Granite 4.2 8B?
GPT-3.5 Turbo 16k costs $3.00 per million input tokens and $4.00 per million output tokens; Granite 4.2 8B costs $0.08 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo 16k or Granite 4.2 8B?
Granite 4.2 8B, with 131K tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo 16k and Granite 4.2 8B?
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Granite 4.2 8B on AA Coding Index, SciCode and LMArena Text: coding. Compare them on price and context instead. Data as of October 23, 2026.