The three strongest models available on DeepInfra right now don’t separate cleanly by capability tier. Kimi K3, Claude Opus 4.8, and GPT-5.6 Sol all score within 3 points of each other on the Artificial Analysis Intelligence Index. All three support one-million-token context windows. All three handle vision. And yet the right choice for a given production workload can be decisive rather than a coin flip because where these models differ, the differences are structural: cost, weight access, long-context pricing, and which specific benchmark category each model was built to lead.

This piece is a working comparison, not a ranking. The goal is to map each model’s actual profile against the workload categories where that profile matters.

The pricing question first

Composite benchmark scores between these three models are close enough that pricing becomes the primary decision axis for most teams. The gap is not subtle: on a 500M input / 200M output monthly workload, K3 runs $3,000 per month cheaper than Opus 4.8 and $4,000 cheaper than Sol. On an output-heavy reasoning workload (100M input / 400M output), the spread is even wider in K3’s favour because its output rate ($15/M) undercuts Opus 4.8 ($25/M) and Sol ($30/M) significantly.

The scenario that most developers underestimate is long-context. Sol’s standard input pricing of $5/M applies only to requests below 272K input tokens. Above that threshold, the full request — every token, not just the overflow — reprices to $10 input / $45 output per million. For pipelines that regularly send long documents, large repo context, or accumulated conversation history, that is not a marginal surcharge; it more than doubles the effective input rate. Opus 4.8 and K3 both bill at flat standard rates regardless of context length.

The one pricing caveat that cuts against K3: it has no non-thinking mode. Every request runs at maximum reasoning effort, which means output token counts tend to be higher than for the same prompt sent to Opus 4.8 or Sol at lower effort. Artificial Analysis’s per-task cost of $0.95 for K3 versus $1.80 for Opus 4.8 accounts for this. K3 still wins on task cost despite the verbosity but teams with hard token budgets per request need to factor in that you can’t dial down K3’s reasoning the way you can with Opus 4.8’s effort controls or Sol’s tier selection.

Where the benchmark profiles actually differ

At the composite level, these models look nearly identical: 56, 57, and 59 on the Artificial Analysis Intelligence Index respectively. The interesting comparison is what sits underneath those numbers.

Reasoning and science

On GPQA Diamond (graduate-level science reasoning, the hardest general-knowledge benchmark regularly run at this tier ) all three are effectively tied: K3 (93.5%), Opus 4.8 (93.6%), and Sol (94.6%). Sol’s 1-point lead is within evaluation noise. For tasks requiring deep domain knowledge across physics, chemistry, and biology, you are not choosing between models here; you are choosing between pricing structures.

Mathematics is where Opus 4.8 breaks away from the other two. Its USAMO 2026 score of 96.7% reflects a meaningful improvement in competition-grade mathematics. Neither K3 nor Sol has published equivalent USAMO numbers, which makes a direct comparison difficult, but the delta from Opus 4.7 to 4.8 on this benchmark is large enough to suggest a genuine capability shift, not a measurement artifact.

Software engineering

This is where the three models diverge most sharply, and where benchmark selection matters most.

SWE-bench Verified, a widely-cited benchmark measuring autonomous code repair on GitHub issues places Sol first (96.2%, third-party), Opus 4.8 second (88.6%), and K3 third (76.8%). The Sol number needs a caveat: OpenAI has not published this figure officially, it comes from vals.ai third-party evaluation, and METR’s safety report on the GPT-5.6 family flagged unusual benchmark-style optimization. SWE-bench Verified is also known to have contamination concerns at frontier performance levels since most models above 85% are within noise of each other on the tasks that are cleanly evaluable.

SWE-bench Pro is the cleaner comparison. It samples 2,294 real repository issues without leaked answers, making contamination harder. Here Opus 4.8 leads (69.2%), Sol follows (64.6%), and K3 hasn’t published a Pro number. The Opus 4.8 lead on Pro is meaningful and less susceptible to gaming than the Verified ranking.

Terminal-Bench 2.1 which tests agentic coding in a real terminal environment puts Sol clearly ahead (88.8% base, 91.9% in Sol Ultra mode) and Opus 4.8 behind (74.6%). That gap is large enough to matter for teams building terminal agents specifically. K3 hasn’t published Terminal-Bench numbers, but third-party testing suggests performance in the Opus 4.8 range rather than Sol’s.

Agentic tasks beyond coding

Opus 4.8’s clearest lead over Sol is on OSWorld-Verified (83.4–87.1% vs Sol’s 62.6% on OSWorld 2.0) — computer-use and GUI agent tasks that go beyond pure code generation. If your workload involves controlling a GUI, navigating multi-step desktop or browser workflows, or coordinating tasks across multiple applications, Opus 4.8 has a documented and significant edge here.

K3’s independent BenchLM evaluation shows its strongest domain is agentic tasks (89.5 average), where it leads Sol (59.1 in BenchLM’s scoring) by a large margin. Its Arena.ai Frontend Code Arena result — first overall, above Fable 5 and Sol — is also worth noting for teams building design-sensitive or UI-generation workflows, where human preference for output quality is the evaluation.

The open-weight question

K3 is the only model in this group you can download. Weights are live on Hugging Face under a Modified MIT license with the single attribution clause only activates above 100 million monthly active users, so it is effectively MIT for all but the largest deployments. Opus 4.8 and Sol are closed; you access them exclusively through the API.

For most teams, this doesn’t change the daily workflow since they are API-calling either way. Where it matters: data sovereignty and regulatory requirements that prohibit sending data to a third-party endpoint; on-premise or air-gapped deployment; fine-tuning on proprietary task data to narrow the capability gap on a specific domain; and multi-provider failover that isn’t dependent on Moonshot’s API uptime. If any of those constraints apply, K3 is the only viable option regardless of the benchmark comparison.

The self-hosting economics do require hardware at scale: K3’s 2.8T parameter count needs a serious multi-node GPU cluster for BF16 serving. FP8 quantization halves the memory footprint but moves you off the reference weights. For teams with the infrastructure, it is a real option. For teams without it, the hosted DeepInfra API is the practical path regardless of license.

A note on transparency

These three models are not equally documented, and that asymmetry affects how much weight to put on different numbers.

Anthropic has published the most complete first-party benchmark set: SWE-bench Verified, SWE-bench Pro, Terminal-Bench 2.1, GPQA Diamond, USAMO 2026, OSWorld-Verified, and GraphWalks F1 are all in the Opus 4.8 system card. Moonshot published self-reported benchmarks for K3 at launch with a full technical report. OpenAI published Terminal-Bench 2.1 and the Coding Agent Index for Sol, but did not publish GPQA Diamond, SWE-bench Verified, USAMO 2026, or Sol’s knowledge cutoff date — most of Sol’s benchmark profile comes from third-party sources.

This doesn’t mean Sol’s third-party scores are wrong. It means they need more time and independent replication before being treated as settled, which is relevant when the vendor-unreported scores (96.2% SWE-bench Verified) are also the ones that make Sol look strongest.

Putting it together

The honest summary is that the right choice across these three models depends on two questions more than any benchmark: what your context length distribution looks like, and whether you need the weights.

If your typical prompt is under 200K tokens, you aren’t doing on-premise deployment, and coding agent performance is your primary quality signal — Sol’s Terminal-Bench lead is real, and its $5/$30 pricing is competitive with Opus 4.8 (after adjusting for output rate). The trade-off is that Sol is the most expensive option per output token and the least documented on third-party benchmarks.

If you regularly work with long documents, large codebases, or accumulated agent context that pushes above 272K tokens — or if OSWorld and GUI agent performance matter — Opus 4.8 is the structurally safer choice. No pricing cliff, better SWE-bench Pro score, published USAMO and GPQA numbers, and the most complete alignment documentation of the three.

If your primary constraint is cost, you need open weights, or you want the strongest agentic and frontend performance the open-weight tier currently offers — K3 at $3/$15 is cheaper than either closed option while matching their composite intelligence scores. The gaps are: no non-thinking mode, a lower SWE-bench Verified score than the closed models, and a thinner safety documentation trail.