LLM Alternatives — 18 Models, Ranked by Price, Effort & Parity
Raw dataset: data.json. Cite this: All AI Ask LLM Switching / Alternatives Dataset, retrieved 2026-10-08.
Every alternative on every page below is a model you can already call through All AI Ask — we route to all of them, so this comparison has no reason to favor any one provider. Each page shows what you save, what you lose, and how much work the switch actually is: a rewrite, a config change, or a drop-in swap.
Need the short provider-level answer? See the OpenAI vs Anthropic pricing, speed, and context table.
What is the best alternative to my LLM?
The 18-model catalog below ranks every switch by price delta, migration effort, and parity gaps instead of crowning one universal winner. 15 swaps need only config changes; the rest trade code changes or rewrites for savings. Open your model for its ranked alternatives with full tradeoff tables.
Model alternatives
| Model | Provider | Blended Price/M (3:1) | Cheapest alternative | Effort |
|---|---|---|---|---|
| GPT-6 Astra | OpenAI | $20.00 | Ministral 8B | config |
| Claude Fable 5.1 | Anthropic | $20.00 | Claude Haiku 5.5 | drop-in |
| Claude Opus 4.8 | Anthropic | $10.00 | Claude Haiku 5.5 | drop-in |
| Claude Opus 5.5 | Anthropic | $8.00 | Claude Haiku 5.5 | drop-in |
| Gemini 3.1 Pro | $4.50 | GPT-6 Luna | config | |
| GPT-6.1 Sol | OpenAI | $4.00 | Ministral 8B | config |
| Claude Sonnet 5.5 | Anthropic | $4.00 | Claude Haiku 5.5 | drop-in |
| Grok 4.7 | xAI | $3.00 | Qwen 3.8 Flash | code-change |
| Grok 4.6 | xAI | $3.00 | Qwen 3.8 Flash | code-change |
| Grok 4.5 | xAI | $3.00 | Qwen 3.8 Flash | code-change |
| GLM-5.3 | Z.ai | $2.15 | Muse Spark 1.3 Contributor | config |
| DeepSeek V4 Pro | DeepSeek | $1.98 | Muse Spark 1.3 Contributor | config |
| Grok 4.3 | xAI | $1.56 | GLM-5.3 Flash | config |
| Gemini 3.8 Flash | $1.50 | GPT-6 Luna | config | |
| Gemini 3.7 Flash | $1.50 | GPT-6 Luna | config | |
| DeepSeek V4.1 Flash | DeepSeek | $0.52 | Muse Spark 1.3 Contributor | config |
| Claude Haiku 5.5 | Anthropic | $0.20 | GPT-6 Luna | config |
| GPT-6 Astra Pro | OpenAI | $20.00 | Ministral 8B | config |
Data verified 2026-10-08. Capped to models with real search demand — see methodology.
Provider alternatives
| Provider | Current models | Price range |
|---|---|---|
| Amazon alternatives | 4 | $0.06 – $1.40 |
| Anthropic alternatives | 6 | $0.20 – $20.00 |
| Cerebras alternatives | 3 | $0.45 – $2.38 |
| DeepSeek alternatives | 2 | $0.52 – $1.98 |
| Google alternatives | 4 | $0.85 – $4.50 |
| Groq alternatives | 3 | $0.13 – $1.60 |
| Meta alternatives | 2 | $0.13 – $2.00 |
| Mistral alternatives | 7 | $0.15 – $3.00 |
| OpenAI alternatives | 6 | $0.20 – $20.00 |
| Qwen alternatives | 4 | $0.23 – $2.80 |
| xAI alternatives | 6 | $1.56 – $3.00 |
| Z.ai alternatives | 2 | $0.24 – $2.15 |
Methodology
Evidence review · verified 2026-08-14
Replacement frontier, portability debt, and cutover capacity
1. Constraint-compiled replacement frontier
Formula / rubric: eligible = every hard gate is pass; Unknown is never Pass.
Dated provenance: Frozen alternatives fixture; three source profiles joined to context, output, modality, reasoning, weights, provider-diversity, and freshness fields; reviewer ledger verified 2026-08-14.
First-party citation: All AI Ask route and evidence ledger
| Fixture | Inputs | Observation / calculation | Decision boundary | State |
|---|---|---|---|---|
| flagship multimodal | context ≥200K; output ≥16K; vision; reasoning; cross-provider; source ≤30d | Candidate A passes 6/6 gates; candidate B fails output; candidate C has unknown freshness. | Do not score closeness after a hard failure or unknown. | PASS — 1 eligible; 2 excluded/unknown. |
| low-cost text | context ≥64K; text; no reasoning requirement; second provider; dated price and evidence | Candidate D passes all gates; Candidate E fails provider diversity; Candidate F has no dated price join. | A missing price is not a zero-cost replacement. | PASS — 1 eligible; 2 excluded/unknown. |
| long-context reasoning | context ≥500K; output ≥32K; reasoning; provider diversity; evidence ≤30d | Candidate G passes context/reasoning; output join is unavailable for Candidate H; Candidate I fails context. | Capacity alone cannot promote a candidate. | UNAVAILABLE — output evidence blocks H. |
2. Portability-debt graph
Formula / rubric: effort = sum(changed edge weights) / sum(applicable edge weights); missing edge evidence is Unavailable.
Dated provenance: Frozen alternatives fixture; five frozen protocol paths with weighted auth, SDK, message, content, stream, tool, cache, batch, usage, error, and deployment edges; reviewer ledger verified 2026-08-14.
First-party citation: All AI Ask route and evidence ledger
| Fixture | Inputs | Observation / calculation | Decision boundary | State |
|---|---|---|---|---|
| OpenAI-compatible → Anthropic Messages | auth .15; SDK .10; messages .15; blocks .15; stream .10; tools .10; cache .10; batch .05; usage .05; errors .05 | Changed auth, message, blocks, stream, cache, and usage weights: (0.15+0.15+0.15+0.10+0.10+0.05)/1.00 = 0.70. | Adapter must preserve content-block order and tool IDs. | PASS WITH REPAIR — typed adapter plus canary required. |
| Gemini native → Bedrock Converse | auth, SDK, content, stream, tools, cache, batch, usage, errors, deployment edges; applicable weight 0.90 | Auth, stream, deployment, and error edges changed; 0.35/0.90 = 38.9% debt. | Bedrock routing is not inferred from a model alias. | PASS — region-specific Converse canary required. |
| Bedrock Converse → self-hosted | SigV4/IAM, SDK, message schema, stream, tools, usage, errors, deployment; deployment revision missing | Changed edge weights total 0.55, but deployment applicability is missing; denominator cannot be closed. | Do not estimate effort across an unjoined host or revision. | UNAVAILABLE — deployment edge evidence missing. |
3. Cutover-and-rollback capacity ledger
Formula / rubric: promotion requires 100/100 accepted requests, side-effect isolation, and all stop-trigger joins; migration overhead is separate from tariffs.
Dated provenance: Frozen alternatives fixture; shadow traffic, reviewer, duplicate-call, usage, and rollback-owner fixtures; reviewer ledger verified 2026-08-14.
First-party citation: All AI Ask route and evidence ledger
| Fixture | Inputs | Observation / calculation | Decision boundary | State |
|---|---|---|---|---|
| 1% shadow / 100-request promotion set | 1 duplicate call per request; 96 accepted; 4 reviewer flags; 18 reviewer minutes; owner: platform-oncall | Accepted coverage = 96/100 = 96%; promotion threshold 98% is not met. | Flagged or side-effectful requests stay out of promotion. | FAIL — rollback remains armed. |
| 5% shadow | 100 requests; 99 accepted; zero isolated side effects; 7 reviewer minutes; unexplained usage join present | 99/100 = 99%, but one unexplained usage join triggers the accounting stop condition. | A high acceptance rate cannot override unexplained usage. | UNAVAILABLE — usage reconciliation blocks promotion. |
| 25% shadow + rollback drill | 100 requests; 100 accepted; 2 reviewer minutes; duplicate calls isolated; owner: migration-lead | All promotion fields join and rollback drill completed in 42 seconds; imported token rates are reported separately. | Full cutover still requires a signed rollback owner. | PASS — eligible for staged promotion. |
Fail-closed rule: an unresolved identity, host, endpoint, artifact, modality, control, workload, acceptance, or accounting join remains Unavailable; no fallback or neighboring route supplies it.
Run the alternatives evidence canary →Effort is a published rule, not a score: same provider is a drop-in; a different, fully OpenAI-compatible provider is a config change; a partially compatible provider (or a model that loses its reasoning-mode parameter) is a code change; a provider with a different wire protocol and auth scheme is a rewrite.
Parity gaps are printed in full, always, before the wins — context, output length, modalities, reasoning mode, prompt caching, batch discounts, free tier, data residency, published SLA, and whether the target trains on your API data. A dimension only counts when the source model or provider actually has it.
Closeness = 0.40·parity + 0.25·effort + 0.25·price advantage + 0.10·speed advantage, min-max normalised across the candidate set. A model missing a speed measurement is never scored as zero on it — that weight is dropped and the rest renormalised.
Model pages are capped at the 18 current models with real search demand (salience ≥ 2 in our internal model dataset) — a deliberate cap against thin, near-duplicate pages. Every alternative on every page is itself a current, non-deprecated model.
FAQ
Or don't migrate at all
One base URL, one key, change the model string — All AI Ask routes to every provider on this page already.
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