Model comparison
GPT-5 (high) vs Mixtral 8x22B Instruct v0.1
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5 (high) #69 (Estimated); Mixtral 8x22B Instruct v0.1 #131 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5 (high) and Mixtral 8x22B Instruct v0.1 share 0 comparable benchmark results. 0 of 8 categories are comparable. 20 results are unique to GPT-5 (high); 0 to Mixtral 8x22B Instruct v0.1.
Updated July 24, 2026- Shared results
- 0
- GPT-5 (high) only
- 20
- Mixtral 8x22B Instruct v0.1 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-5 (high) and Mixtral 8x22B Instruct v0.1 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for Mixtral 8x22B Instruct v0.1 yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GPT-5 (high) is priced at $1.25 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Mixtral 8x22B Instruct v0.1. GPT-5 (high) has the larger context window at 128K, compared with 64K for Mixtral 8x22B Instruct v0.1.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5 (high) | Mixtral 8x22B Instruct v0.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5 (high)$1.25 input / $10 output | Mixtral 8x22B Instruct v0.1$0 input / $0 output | Mixtral 8x22B Instruct v0.1 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5 (high)83 tok/s | Mixtral 8x22B Instruct v0.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5 (high)36.28 s | Mixtral 8x22B Instruct v0.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5 (high)128K | Mixtral 8x22B Instruct v0.164K | GPT-5 (high) lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5 (high) | Mixtral 8x22B Instruct v0.1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 34.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.4% | — | Not comparable |
| AA-HLESource | 26.5% | — | Not comparable |
| AA-Omniscience IndexSource | -8.1% | — | Not comparable |
| AA-Omniscience AccuracySource | 40.7% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 82.1% | — | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5 (high) | Mixtral 8x22B Instruct v0.1 | Result |
|---|---|---|---|
| AA MATH-500Source | 99.4% | — | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5 (high) | Mixtral 8x22B Instruct v0.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.1% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-5 (high) and Mixtral 8x22B Instruct v0.1 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-5 (high) and Mixtral 8x22B Instruct v0.1 today?
GPT-5 (high): $1.25 input / $10.00 output per 1M tokens Mixtral 8x22B Instruct v0.1: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.