GPT-3.5 Turbo 16k vs Mixtral 8x22B Instruct
GPT-3.5 Turbo 16k costs 1.5x less per output token than Mixtral 8x22B Instruct ($4.00 vs $6.00 per million). Updated October 22, 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
- $6.00
GPT-3.5 Turbo 16k or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo 16k for output tokens 1.5x cheaper ($4.00 vs $6.00 per million).
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Mixtral 8x22B Instruct on LiveCodeBench (Artificial Analysis run), SciCode and AA Intelligence Index. 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 Mixtral 8x22B Instruct has a AA Intelligence Index result, 5.7.
Directional only
Long documents
Listed context window
Mixtral 8x22B Instruct
Mixtral 8x22B Instruct takes 66K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
GPT-3.5 Turbo 16k $0.0050, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo 16k not benchmarked, Mixtral 8x22B 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
- GPT-3.5 Turbo 16k $4.00 against Mixtral 8x22B Instruct $6.00 per million output tokens, 1.5x apart.
- Context window
- GPT-3.5 Turbo 16k takes 16K tokens, Mixtral 8x22B Instruct 66K.
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 Index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 14.8% | Only Mixtral 8x22B Instruct measured |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.7 | Only Mixtral 8x22B Instruct measured |
| Epoch Capabilities Index | Not measured | 122.0 | Only Mixtral 8x22B Instruct measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 33.2% | Only Mixtral 8x22B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 20 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 53.7% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| DTBench | Not measured | 55.1% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 45 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
Same cost
Repository review
50K input + 3K output tokens
Mixtral 8x22B Instruct costs less
Agent loop
200K input + 10K output tokens
Mixtral 8x22B Instruct 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 | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | August 28, 2023 | April 17, 2024 |
| Context window | 16K | 66K |
| Input $/Mtok | $3.00 | $2.00 |
| Output $/Mtok | $4.00 | $6.00 |
| 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 GPT-3.5 Turbo 16k better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo 16k for output tokens 1.5x cheaper ($4.00 vs $6.00 per million).
Which is cheaper, GPT-3.5 Turbo 16k or Mixtral 8x22B Instruct?
GPT-3.5 Turbo 16k costs $3.00 per million input tokens and $4.00 per million output tokens; Mixtral 8x22B Instruct costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo 16k or Mixtral 8x22B Instruct?
Mixtral 8x22B Instruct, with 66K tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo 16k and Mixtral 8x22B Instruct?
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Mixtral 8x22B Instruct on LiveCodeBench (Artificial Analysis run), SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 22, 2026.